EN.560.691 CaSE Graduate Seminar
Graduate students are expected to register for this course each semester. Both internal and outside speakers are included. The first three meetings are dedicated to presentations by the faculty from the Civil and Systems Engineering Department.
Civil and Systems Engineering, PhD
School of Engineering
The PhD program at the Johns Hopkins University Department of Civil and Systems Engineering aims to inspire the leaders of tomorrow to take on the challenge of creating and sustaining engineered systems that underpin our society , from the built environment to public health systems . Our graduate students work with faculty members who are world-renowned leaders in their fields and contribute to research that has a tremendous impact on society. Focal research areas in the department include structural engineering, structural mechanics, probabilistic methods, hazards management, and systems engineering. Examples of current projects include fracture and fatigue in materials and structural systems, design of additively manufactured architected materials, earthquake engineering, and applying systems approaches to improving patient flow in hospitals and predicting virus outbreak.
Civil Engineering, Master of Science in Engineering (MSE)
School of Engineering
Our Master of Science in Engineering (M.S.E.) in Civil Engineering program develops a sound understanding of the scientific principles upon which engineering research and practice are based. Different aspects of learning are integrated through classroom, laboratory instruction, and independent study experiences. Graduates of the program possess critical thinking skills, the ability for both independent and team problem-solving, and a sense of the excitement of engineering creativity and design. The program also develops communication skills necessary for the graduates to function in teams and to deal with other professions in public and private arenas. Their progressive education furthers student understanding of the context in which engineering is practiced in modern society. Our Master’s program combines fundamental training with real-world experience. Thus, the program educates leaders for tomorrow, providing the tools and perspectives for a lifetime of learning, opportunities, and professional advancement. Students build a knowledge base with coursework in systems, mechanics, structures, computational methods, and uncertainty quantification. Apply that knowledge in our internship program while networking with professional engineers and gain valuable experience with industry.
EN.560 (Civil and Systems Engineering)
http://e-catalogue.jhu.g.sjuku.top/course-descriptions/civil_engineering/
EN.560.100. Civilization Engineered: Structures and Systems. 3 Credits. Civilizations rely on engineered structures and infrastructure systems to supply their basic needs, including water, energy, transportation, and shelter. This course will examine the past, present, and future of the engineering solutions on which civilizations rely, and the evolving technological, environmental, and societal challenges to which our current solutions must adapt. Through lectures and hands-on activities, students will learn fundamental engineering concepts and methods of graphical communication, as well as an introduction to physical and computational modeling of structures and infrastructure systems. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.101. Civilization Engineered: Data-driven Solutions for Communities. 3 Credits. Modern civilizations are inundated with data, which presents challenges related to the quantity and quality of data, but also opportunities for engineers to improve people's lives through data-informed design. Increasingly, data is being leveraged to help create solutions for society's grand challenges in the areas of sustainable and resilient cities, human safety and security, decision-making in healthcare, future energy infrastructure, even space exploration and habitation. This course will take a deep-dive into data - how to collect, process, visualize, model, and interpret it – with the goal of designing and evaluating solutions for the grand challenges that will impact our collective future. Coding will be emphasized in this class. The ePortfolio tag(s) on this course signify that there are one or more assignments offered in the course that provide students with the opportunity to be assessed for proficiency in completion of the relevant ePortfolio requirement(s). Prerequisite(s): EN.500.113 or EN.500.133 Distribution Area: Engineering EN Foundational Abilities: Oral Communication ePortfolio (FA1.2eP) EN.560.191. CaSE Collaborative. 0.5 Credits. From sketching to 3D printing, students in this course will work directly with the tools that civil and systems engineers use to plan and communicate their ideas. Hands-on learning activities will help students develop these skills, with an emphasis on communication and collaboration using graphical tools such as CAD and GIS software and physical specimens fabricated with manual construction and 3D printing. Distribution Area: Engineering, Natural Sciences AS Foundational Abilities: Science and Data (FA2) EN.560.192. CaSE Cornerstone Design Project. 1 Credit. Through a semester-long project, students in this course will practice the engineering design process as they work with a community partner to develop a proposal for the improvement or development of a parcel of land in Baltimore City (e.g. a vacant lot, parkland, etc.). Students will present their proposals at the Whiting School’s Design Day. The ePortfolio tag(s) on this course signify that there are one or more assignments offered in the course that provide students with the opportunity to be assessed for proficiency in completion of the relevant ePortfolio requirement(s). Distribution Area: Engineering EN Foundational Abilities: Conceiving of and Realizing Projects ePortfolio (FA6eP) EN.560.201. Statics & Mechanics of Materials. 3 Credits. This course combines statics - the basic principles of classical mechanics applied to the equilibrium of particles and rigid bodies at rest, under the influence of various force systems - with mechanics of materials - the study of deformable bodies and the relationships between stresses and deformations within those bodies. Fundamental concepts in statics include the proper use of free body diagrams, the analysis of simple structures, centroids and centers of gravity, and moments of inertia. The study of mechanics of materials will focus on the elastic analysis of axial force, torsion, and bending members to determine corresponding stresses and strains. Stress transformations and principal stresses will be introduced.For most majors, students are required to register for both 560.201 Statics and Mechanics of Materials and 560.211 Statics and Mechanics of Materials Laboratory. Prerequisite(s): AS.171.101 OR AS.171.105 OR AS.171.107 OR ( EN.530.123 AND EN.530.124 ) or instructor permission. Corequisite(s): EN.560.211 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.211. Statics and Mechanics of Materials Laboratory. 1 Credit. The complementary laboratory course for and required corequisite to EN.560.201 Statics and Mechanics of Materials. For most majors, students are required to register for both 560.201 Statics and Mechanics of Materials and 560.211 Statics and Mechanics of Materials Laboratory. Prerequisite(s): Students must have completed Lab Safety training prior to registering for this class. To access the tutorial, login to myLearning and enter Laboratory Safety Introductory Course in the Search Box to access the proper course. Click here to access the Laboratory Safety Introductory Course Corequisite(s): EN.560.201 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.240. Uncertainty, Reliability and Decision-making. 3 Credits. This course covers the essentials of probability and statistics with an emphasis on their use for reliability, risk, and decision making for civil and systems engineering applications. Topics include the basics of probability theory (random variables, moments, single and multi-variate distribution functions), an introduction to occurrence probabilities and extreme value statistics and their use in assessing risk of civil infrastructure systems, and introductory concepts in structural reliability and reliability-based design. Prerequisite(s): AS.110.109 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.560.250. Intro to Mathematical Decision Making. 3 Credits. This first course in mathematical decision-making introduces optimization models and their role in solving complex problems. The methods are motivated by a set of real-world problems from various domains including Transportation, Energy, Health, Management, and Space, among others. The course covers linear and integer optimization formulations, solution algorithms, sensitivity analysis, network models, simulation examples, and hands-on solution techniques and coding. Prerequisite(s): EN.553.291 AND EN.500.113 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.560.291. CaSE Coding. 0.5 Credits. Having learned basic Python programming skills in Gateway Computing, CaSE Coding will provide an opportunity for students to apply and further develop their coding skills by using them to analyze, interpret, and visualize real-world data from the materials, structures, and infrastructure systems that make up our built environment. Prerequisite(s): AS.110.109 AND EN.500.113 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.292. CaSE Research. 0.5 Credits. An introduction to the research process, students in this project-based course will develop an appreciation for the role of research in our society and will learn the tools indispensable to researchers, including how to conduct literature reviews, how to read and write technical literature, as well as how to formulate and test a research hypothesis. Students will explore the research process through a variety of methods including as an exercise in uncertainty quantification. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.301. Structural Systems I. 3 Credits. This course will introduce students to the structural design workflow from concept and ideation to structural modeling and analysis to member and connection design using the reliability-based limit states approach. This first course in a two-course sequence will focus on the analysis and design of structural systems composed primarily of axial force members (e.g. trusses, cables, and arches). Connections to mechanics-based principles will be emphasized and practical applications using common structural materials such as timber, steel, and reinforced concrete will be covered. Prerequisite(s): EN.560.201 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.302. Structural Systems II. 3 Credits. This second course in the two-course structural systems sequence will reinforce the structural design workflow from concept and ideation to structural modeling and analysis to limit states design, but with a focus on the analysis and design of structural systems composed of bending members (e.g. frames). Connections to mechanics-based principles will again be emphasized and practical applications using common structural materials such as timber, steel, and reinforced concrete will be covered. Prerequisite(s): EN.560.301 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.305. Soil Mechanics. 4 Credits. Basic principles of soil mechanics. Classification of soils. Compaction theory. Consolidation seepage and settlement analysis. Stress-strain and shear strength of soils. Introduction to earth pressure theories and slope stability analysis. Prerequisite(s): Students must have completed Lab Safety training prior to registering for this class. To access the tutorial, login to myLearning and enter ASEN in the Search Box to access the proper course. Click here to access the Laboratory Safety Introductory Course ; EN.560.201 AND EN.560.211 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.312. Electromagnetism & Sensors Lab. 1 Credit. Electricity and magnetism underpins much of modern engineering, as an alternative or addendum to classical Physics this, largely, hands-on laboratory course exposes engineers to the principles of electromagnetism and how they are leveraged in the modern world with a focus on their application in infrastructure and sensor networks. Prerequisite(s): Students must have completed Lab Safety training prior to registering for this class. To access the tutorial, login to myLearning and enter 458083 in the Search box to locate the appropriate module. Distribution Area: Engineering, Natural Sciences EN.560.315. Data Science for Systems Engineers. 3 Credits. An advanced data analytics course intended for students who have previous experience with data science, probability, and statistics. This course looks at the principles and techniques of data science tailored towards applications in systems engineering contexts, specifically those related to major societal challenges including resilient cities, human safety and security, decision-making and healthcare, future energy infrastructure, and space exploration and habitation. Prerequisite(s): EN.500.115 Distribution Area: Engineering EN.560.330. Foundation Design. 3 Credits. Application of soil mechanics theory and soil test results to the analysis and design of foundations for structures; retaining walls; embankments; design of pile and shallow footing foundations; slope stability. Prerequisite(s): EN.560.305 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.355. Dynamical Systems. 3 Credits. This course will introduce students to the modeling and analysis of dynamical systems using analytical, numerical and qualitative (geometric) techniques. The course will focus on dynamical systems arising in mechanics and vibrations, global climate models and infectious disease modeling. The following topics will be covered: first order systems, phase space, bifurcations, numerical integration, second order linear systems, stability, finite differences, nonlinear systems, higher order systems, introduction to chaos. Prerequisite(s): EN.553.291 AND ( AS.171.101 OR AS.171.105 OR AS.171.107 ) Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.560.362. Engineering Mechanics and Materials. 3 Credits. This course will provide an in-depth exploration of the mechanics of solid and liquid materials with a focus on constitutive equations. The course will cover both linear and nonlinear equations and their applications to solid and liquid materials under various loading conditions. Topics will include stress and strain, (visco-)elasticity and plasticity, failure criteria, and fluid mechanics. Students will study the derivation and use of constitutive equations for materials such as metals, polymers, and (non-)Newtonian liquids. Prerequisite(s): EN.553.291 AND EN.560.201 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.391. CaSE Careers I. 0.5 Credits. CaSE Careers I provides students with opportunities to explore the wide range of career paths available to civil and systems engineering graduates (e.g. consulting, academia, government, industry, and construction) through invited speakers, field trips to design offices / construction sites, and attendance at professional society meetings. Topics related to engineering ethics, professional licensure, and other current professional issues are also discussed. The ePortfolio tag(s) on this course signify that there are one or more assignments offered in the course that provide students with the opportunity to be assessed for proficiency in completion of the relevant ePortfolio requirement(s). Distribution Area: Engineering EN Foundational Abilities: Writing ePortfolio (FA.1.1eP), Ethical Reflection ePortfolio (FA5eP) EN.560.392. CaSE Careers II. 0.5 Credits. CaSE Careers II provides students with opportunities to explore the wide range of career paths available to civil and systems engineering graduates (e.g. consulting, academia, government, industry, and construction) through invited speakers, field trips to design offices / construction sites, and attendance at professional society meetings. Topics related to engineering economics and other current professional issues are also discussed. Distribution Area: Engineering EN.560.401. Design Theory and Practice. 3 Credits. First course in the two-course senior design capstone sequence for civil and systems engineering majors. Students will learn about various engineering design theories and their applications. They will also be assigned to small teams tasked with proposing engineering solutions to societal challenges in areas such as transportation, shelter, energy, and healthcare. The course will culminate in students’ formal design proposals that will be finalized in EN.560.402 CaSE Capstone Design Project. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.402. CaSE Capstone Design Project. 3 Credits. Second course in the two-course senior design capstone sequence for civil and systems engineering majors. Following 560.401 Design Theory and Practice, in EN.560.402 students will finalize, document, and present their capstone design projects in small teams to fellow students, faculty, staff, and industry professionals at the Whiting School’s Design Day. Prerequisite(s): EN.560.401 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.410. Drivers of Technological Change. 3 Credits. Technological innovation is everywhere—from smartphones and ChatGPT to solar panels and electric vehicles—but how well do we understand the processes that turn novel ideas into widely adopted technologies? This interdisciplinary course introduces models of technological change and explores their implications for engineers, entrepreneurs, and policymakers seeking to advance energy and other infrastructure systems. Students will engage with conceptual and quantitative models that explain how technologies emerge, improve in performance (e.g., through cost reductions), diffuse, and contribute to economic growth and human development. Case studies on energy, transportation, and general-purpose technologies ground abstractions in practical contexts. Key course themes include: (a) how technological characteristics influence innovation pathways; (b) the opportunities and limitations of using historical trends to guide future innovation; and (c) how modeling choices shape efforts to manage and accelerate technological change. Distribution Area: Engineering EN.560.421. Architectural Engineering - Form, Function and Technology. 3 Credits. This course will cultivate broad knowledge of the use of engineering principles in the art of architecture. Fundamental definitions of architecture in the basic provision of shelter and social use are paired with aesthetics and cultural heritage. The course emphasizes structural frameworks and systems within the Civil Engineering curriculum, while expanding upon their critical intersections with the highly varied specialized components and systems of modern architecture, and the corresponding community of specialists that represent them. Topics include a historical view of the evolution of specialization in architecture, a quantitative review of loads and resistance systems, architectural and structural determinants of form, the function and aesthetics of building surface, and an introduction to environmental systems and their role in design sustainability. The class will include a trip to Fallingwater, the house designed by Frank Lloyd Wright, in western Pennsylvania, which stands as an iconic example of American architecture and a complex example of architectural engineering. This course is co-listed with? EN.560.621 . Prerequisite(s): EN.560.302 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.424. Renewable Energy Structures. 3 Credits. This course provides an introduction to the structural engineering principles underlying renewable energy systems, focusing on both the demands placed on these structures and the capacity calculations necessary for their safe and efficient design. Students will explore the challenges involved in scaling up renewable energy infrastructure to meet national and global energy demands. The course will cover a wide variety of renewable energy structures including: dams, solar support structures, on- and off-shore wind energy structures, transmission structures, structures for energy storage, and other novel renewable energy structures (e.g. solar chimneys, structures for carbon capture, etc.). A significant portion of the course will be dedicated to a more open-ended design effort, where students will propose, design, and analyze a renewable energy structure, with an emphasis on novel or emerging technologies and/or scale-up. Distribution Area: Engineering EN.560.429. Preservation Engineering: Theory and Practice. 3 Credits. The renovation of existing buildings often holds many advantages over new construction, including greater economy, improved sustainability, and the maintenance of engineering heritage and architectural character in our built environment. Yet, the renovation of existing structures presents many challenges to structural engineers. These challenges include structural materials that are no longer in widespread use (e.g., unreinforced masonry arches and vaults, cast iron, and wrought iron) as well as structural materials for which analysis and design practices have changed significantly over the last half-century (e.g., wood, steel, and reinforced concrete).This course will examine structures made of a wide variety of materials and instruct the student how to evaluate their condition, determine their existing capacity, and design repairs and/or reinforcement. The investigation and analysis procedures learned from this course may then be applied to create economical and durable structural alterations that allow for the reuse of older buildings. Site visits near Homewood campus will supplement lectures. Co-listed with 560.629. Prerequisite(s): EN.560.301 AND EN.560.302 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.431. Preservation Engineering II: Theory and Practice. 3 Credits. Building on the content in Preservation Engineering I: Theory and Practice, this course will begin with materials introduced at the start of the Industrial Revolution--namely with the beginning of the use of iron materials as major structural elements within buildings. The course will continue with the introduction of cast iron, wrought iron, and finally, structural steel members. After introducing iron materials the course will continue with the early use of reinforced concrete as a major structural material. The course will discuss the historic structural analysis methods associated with such materials and contrast such methods with more modern analytical approaches. It will also discuss concrete deterioration and repair methods. Concepts related to masonry facade investigation and repair will be presented along with the analytical methods associated with thin-shell masonry construction from the 19th and 20th centuries. The course will conclude with a review of the assessment and retrofit of historic foundations.Course is co-listed with EN.560.631 and EN.565.631 . Prerequisite(s): EN.560.429 OR Permission from the instructor. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.434. Structural Fire Engineering. 3 Credits. This course will discuss the analysis and design of structures exposed to fire. It will cover the fundamentals of fire behavior, heat transfer, the effects of fire loading on materials and structural systems, and the principles and design methods for fire resistance design. Particular emphasis will be placed on advanced methods and numerical modeling tools for performance-based design. Applications of innovative methods for structural fire design in buildings and other structures will also be presented. Course is co-listed with graduate-level EN.560.634 . Prerequisite(s): EN.560.302 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.445. Advanced Structural Analysis. 3 Credits. Matrix methods for the analysis of statistically indeterminate structures such as beams, plane and space trusses, and plane and space frames. Stiffness and flexibility methods. Linear elastic analysis and introduction to nonlinear analysis. Co-listed with EN.560.619 . Prerequisite(s): EN.560.301 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.448. Energy Systems and Policy. 3 Credits. In this course, you will develop an understanding—and a technically- and socially-deep working knowledge—of our energy technologies, policies, and options. This will include analysis of the different opportunities and impacts of energy systems that exist within and between groups defined by national, regional, household, ethnic, and gender distinctions. Analysis of the range of current and future energy choices will be stressed, as well as the role of energy in determining local environmental conditions and the global climate. EN.560.449. Energy Systems. 3 Credits. This course revolves around the grid integration of renewable energy systems and operations of energy systems with renewables. The main emphasis is on grid level effects of renewable energy, particularly solar and wind power systems, and how these effects can be analyzed using mathematical modeling and modern software tools. The course begins with an introduction to basic power system concepts (transmission/distribution system modeling, power transformers, conventional and renewable generation technologies) along with power flow analysis and optimization. Following that, the course considers applications of optimal power flow and its variants to electricity market operations. An important component of this course is a guided project that will be carried out by students in small groups; each group will choose a real-world energy system to research and analyze and will present their findings at the end of the semester. Prior knowledge of circuits (including operations with complex numbers), linear algebra, calculus, and optimization is helpful, but not required. This course is co-listed with EN.560.649 . Prerequisite(s): AS.110.202 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.560.450. Operations Research. 3 Credits. An introduction to operations research and its applications. The course will review the basics of mathematical modelling, linear programming, primal and dual Simplex methods, post-optimization analysis, decomposition methods, and heuristic methods along with sample applications. Recommended course background ( EN.553.291 or AS.110.201 ) and AS.110.109 or equivalent. This course is co-listed with EN.560.650 . Prerequisite(s): Students who have taken EN.560.650 are not eligible to take EN.560.450 . Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.451. Smart Transportation and Autonomous Vehicles. 3 Credits. This course offers an in-depth exploration of the evolving landscape of smart transportation systems and autonomous vehicle technologies. Students will study the integration of intelligent transportation systems (ITS) and automation, with a focus on the development, challenges, and future of autonomous vehicles (AVs) within open-world traffic settings. Key topics include sensor technologies, data quality control and bias mitigation, data processing algorithms, machine learning, vehicle-to-everything (V2X) communication, human-machine interaction, and ethical considerations in technology deployment. Additionally, the course will cultivate students with advanced data processing skills and the role of data science in transportation, emphasizing data-driven solutions for safety, mobility, and sustainability. By the end of the course, students will have gained a comprehensive understanding of how smart technologies are shaping the future of transportation and the role of AVs in fostering safer, more efficient, and sustainable urban mobility systems. Prerequisite(s): Students who are currently enrolled in, or have already completed EN.560.651 , are not eligible to enroll in EN.560.451 . Distribution Area: Engineering EN.560.453. An Introduction to Network Modeling. 4 Credits. Many real-world problems can be modeled using network structures, and solved using tools from network theory. For this reason, network modeling plays a critical role in various disciplines ranging from physics and mathematics, to biology and computer science, and almost all areas of social science. This course will provide an introduction to network theory, network flow algorithms, modeling processes on networks and examples of empirical network applications spanning transport, health and energy systems. Co-listed with EN.560.653 . Prerequisite(s): EN.553.291 AND EN.500.113 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.560.457. System Dynamics. 3 Credits. System dynamics is a versatile analytical framework to understand and tackle problems which involve complex interactions among multiple variables and constraints. This course introduces the basics of systems thinking and system dynamics modeling and analysis. Qualitative and quantitative tools are discussed. Students will learn to identify and formulate system's structure and simulate their behavior using specialized software in order to develop potential intervention strategies. Fields of applications include engineering, climate change, resilience, logistics, public policy analysis, business, and decision-making. Prerequisite(s): AS.110.109 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.560.458. Natural Disaster Risk Modeling. 3 Credits. This course provides an in-depth discussion of the simulation of disaster risk on socio-technical systems (from countries to cities). The course covers the algorithmic structure of catastrophe models; modeling of the intensity fields of hurricanes, earthquakes, and floods; methods to develop building and infrastructure vulnerability functions, structure of exposure layers, and estimation of post-disaster injuries and casualties. The students learn to produce basic stochastic catalogs from where risk metrics are calculated. Finally, the risk-reduction policy formulation process is presented using as input the catastrophe model-generated information. The course has a strong real-life application side analyzing World Bank risk reduction projects. Students will gain introductory experience in the use of GIS, Matlab, and R. This course is co-listed with EN.560.658 . Prerequisite(s): EN.560.240 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.560.459. Production Systems Analysis. 3 Credits. Planning for manufacturing and service industries is critical for efficiently utilizing resources to produce cost-effective goods and services. This course delves into the fundamentals, models, and techniques required for planning, controlling, and optimizing the performance of manufacturing systems. The curriculum focuses on the trade-offs between key measures, like costs, cycle time, throughput, capacity, work-in-process, inventory, and variability. The course utilizes analytical approaches (linear programming, simulation, probability, and statistics) and coding (Python). Co-listed with EN.560.679. Prerequisite(s): Students may take only EN.560.479 or EN.560.679, but not both.; EN.500.113 AND EN.560.240 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.462. Failure Mechanics in Materials. 3 Credits. This course provides an overview of the various modes of failure found in traditional and non-traditional structural materials. The concepts will be demonstrated through computational models and physical demonstrations. This is the second course in a two-semester engineering mechanics sequence that starts with 560.362 Engineering Mechanics and Materials. These courses may be taken out of sequence only with the instructor’s permission. Prerequisite(s): EN.560.201 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.560.501. Undergraduate Research. 1 - 3 Credits. Students who participate in ongoing research activities may register for this course with permission of their supervising faculty member. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.560.511. Group Undergraduate Research. 1 - 3 Credits. Students who participate in ongoing research activities may register for this course with permission of their supervising faculty member. This course differs from EN.560.501 in that it includes a weekly research group meeting that students are expected to attend. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.560.526. Independent Study - Civil and Systems Engineering. 1 - 3 Credits. Undergraduate students pursue research problems with a faculty supervisor. Although the research is under the direct supervision of a faculty member, students are encouraged to pursue the research as independently as possible. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.560.601. Applied Math for Engineers. 3 Credits. This course presents a broad survey of the basic mathematical methods used in the solution of ordinary and partial differential equations: linear algebra, power series, Fourier series, separation of variables, integral transforms. Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.560.604. Introduction to Solid Mechanics. 3 Credits. Basic solid mechanics for structural engineers. Stress, strain and constitutive laws. Linear elasticity and viscoelasticity. Introduction to nonlinear mechanics. Static, dynamic and thermal stresses. Specialization of theory to one- and two-dimensional cases: plane stress and plane strain, rods, and beams. Work and energy principles; variational formulations. EN.560.610. Drivers of Technological Change. 3 Credits. Technological innovation is everywhere—from smartphones and ChatGPT to solar panels and electric vehicles—but how well do we understand the processes that turn novel ideas into widely adopted technologies? This interdisciplinary course introduces models of technological change and explores their implications for engineers, entrepreneurs, and policymakers seeking to advance energy and other infrastructure systems. Students will engage with conceptual and quantitative models that explain how technologies emerge, improve in performance (e.g., through cost reductions), diffuse, and contribute to economic growth and human development. Case studies on energy, transportation, and general-purpose technologies ground abstractions in practical contexts. Key course themes include: (a) how technological characteristics influence innovation pathways; (b) the opportunities and limitations of using historical trends to guide future innovation; and (c) how modeling choices shape efforts to manage and accelerate technological change. Distribution Area: Engineering EN.560.617. Deep Learning for Physical Systems. 3 Credits. The primary objective of this course is to foster a deep and holistic comprehension of the concepts surrounding deep learning, as well as their practical applications within engineering systems. This course encompasses a broad spectrum of methodologies, notably emphasizing the utilization of physics-informed and data-driven techniques for both time-dependent and static Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs). We delve into the study of multi-layer perceptrons, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and autoencoders, exploring their roles in discerning patterns within data, providing solutions even in scenarios with limited data availability, and learning a family of equations using one network architecture. Through this course, students will acquire the skills to proficiently employ these methods in tackling a wide-ranging spectrum of computational challenges prevalent in domains like solid mechanics, biomechanics, and systems engineering. Proficiency in Python coding is essential for this course. To make the most of this course, it's important to have a basic understanding of Linear Algebra and Probability. Distribution Area: Engineering EN.560.618. Probabilistic Methods in Civil Engineering and Mechanics. 3 Credits. Covers probabilistic computational modeling in civil engineering and mechanics: Monte Carlo simulation, sampling methods and variance reduction techniques, simulation of stochastic processes and fields, and expansion methods. Applications to stochastic finite element, uncertainty quantification, reliability analysis, and model verification and validation. EN.560.619. Advanced Structural Analysis. 3 Credits. Matrix methods for the analysis of statistically indeterminate structures such as beams, plane and space trusses, and plane and space frames. Stiffness and flexibility methods. Linear elastic analysis and introduction to nonlinear analysis. Distribution Area: Engineering EN.560.621. Architectural Engineering - Form, Function and Technology. 3 Credits. This course will cultivate broad knowledge of the use of engineering principles in the art of architecture. Fundamental definitions of architecture in the basic provision of shelter and social use are paired with aesthetics and cultural heritage. The course emphasizes structural frameworks and systems within the Civil Engineering curriculum, while expanding upon their critical intersections with the highly varied specialized components and systems of modern architecture, and the corresponding community of specialists that represent them. Topics include a historical view of the evolution of specialization in architecture, a quantitative review of loads and resistance systems, architectural and structural determinants of form, the function and aesthetics of building surface, and an introduction to environmental systems and their role in design sustainability. The class will include a trip to Fallingwater, the house designed by Frank Lloyd Wright, in western Pennsylvania, which stands as an iconic example of American architecture and a complex example of architectural engineering. This course is co-listed with EN.560.421 . Distribution Area: Engineering EN.560.622. Introduction to Uncertainty Quantification. 3 Credits. The course introduces the theory and practice of uncertainty quantification. Methods for quantifying aleatory and epistemic uncertainty are considered, probabilistic and non-probabilistic approaches are discussed. The course introduces: propagation of uncertainty including statistical sampling methods, surrogate modeling, and numerical methods; inverse uncertainty quantification using Bayesian methods; global sensitivity analysis; and reliability/probability of failure analysis. The course is project-based and will require prior knowledge of both probability theory and coding (preferably in Python). Distribution Area: Engineering EN.560.623. Bridge Engineering. 3 Credits. This course will explore bridge design and analysis by studying local bridges of various forms, materials, and load demands. Topics include an overview of the history of bridge engineering, an introduction to the AASHTO Standard Specifications for Highway Bridges, analysis techniques and load ratings, bridge details, and substructure design. Distribution Area: Engineering EN.560.624. Renewable Energy Structures. 3 Credits. This course provides an introduction to the structural engineering principles underlying renewable energy systems, focusing on both the demands placed on these structures and the capacity calculations necessary for their safe and efficient design. Students will explore the challenges involved in scaling up renewable energy infrastructure to meet national and global energy demands. The course will cover a wide variety of renewable energy structures including: dams, solar support structures, on- and off-shore wind energy structures, transmission structures, structures for energy storage, and other novel renewable energy structures (e.g. solar chimneys, structures for carbon capture, etc.). A significant portion of the course will be dedicated to a more open-ended design effort, where students will propose, design, and analyze a renewable energy structure, with an emphasis on novel or emerging technologies and/or scale-up. Distribution Area: Engineering EN.560.629. Preservation Engineering I: Theory and Practice. 3 Credits. The renovation of existing buildings often holds many advantages over new construction, including greater economy, improved sustainability, and the maintenance of engineering heritage and architectural character in our built environment. Yet, the renovation of existing structures presents many challenges to structural engineers. These challenges include structural materials that are no longer in widespread use (e.g., unreinforced masonry arches and vaults, cast iron, and wrought iron) as well as structural materials for which analysis and design practices have changed significantly over the last half-century (e.g., wood, steel, and reinforced concrete).This course will examine structures made of a wide variety of materials and instruct the student how to evaluate their condition, determine their existing capacity, and design repairs and/or reinforcement. The investigation and analysis procedures learned from this course may then be applied to create economical and durable structural alterations that allow for the reuse of older buildings. Site visits near Homewood campus will supplement lectures. co-listed with 560.429. Distribution Area: Engineering EN.560.630. Structural Dynamics. 3 Credits. Functional and computational examination of elastic and inelastic single degree of freedom systems with classical and non-classical damping subject to various input excitations including earthquakes with emphasis on the study of system response. Extension to multi-degree of freedom systems with emphasis on modal analysis and numerical methods. Use of the principles of structural dynamics in earthquake response. EN.560.631. Preservation Engineering II: Theory and Practice. 3 Credits. Building on the content in Preservation Engineering I: Theory and Practice, this course will begin with materials introduced at the start of the Industrial Revolution--namely with the beginning of the use of iron materials as major structural elements within buildings. The course will continue with the introduction of cast iron, wrought iron, and finally, structural steel members. After introducing iron materials the course will continue with the early use of reinforced concrete as a major structural material. The course will discuss the historic structural analysis methods associated with such materials and contrast such methods with more modern analytical approaches. It will also discuss concrete deterioration and repair methods. Concepts related to masonry facade investigation and repair will be presented along with the analytical methods associated with thin-shell masonry construction from the 19th and 20th centuries. The course will conclude with a review of the assessment and retrofit of historic foundations.This course is co-listed with EN.560.431 and EN.565.631 . Distribution Area: Engineering EN.560.632. Structural Stability. 3 Credits. Concepts of stability of equilibrium, stability criteria, work energy and variational methods. Elastic buckling of columns, beams, frames, and plates. Introduction to inelastic and dynamic buckling. EN.560.633. Investigations, Diagnosis, and Rehabilitation. 3 Credits. Why do buildings deteriorate, and how do we address this problem? This course examines the deterioration (by human and nature) of building materials and systems. Through lectures and a field trip, students will learn how to set up and execute an investigation, study the symptoms, diagnose the problems, determine what kinds of tests are needed, design the necessary repairs, and maintain existing systems. This course is co-listed with Engineering for Professionals EN.565.633 . Distribution Area: Engineering EN.560.634. Structural Fire Engineering. 3 Credits. This course will discuss the analysis and design of structures exposed to fire. It will cover the fundamentals of fire behavior, heat transfer, the effects of fire loading on materials and structural systems, and the principles and design methods for fire resistance design. Particular emphasis will be placed on the advanced modeling and computational tools for performance-based design. Applications of innovative methods for fire resistance design in large structural engineering projects, such as stadiums and tall buildings, will also be presented. Distribution Area: Engineering EN.560.636. Lateral Forces: Analysis and Design of Building Structures. 3 Credits. From earthquakes to wind events, lateral forces constitute some of the most extreme loading conditions for which new and existing building structures must be analyzed and designed to resist. This course provides a fundamental yet practical introduction to the development and application of earthquake and wind loadings on building structures, the dynamic response and behavior of structures to lateral forces, and the bases and requirements for ductile design and detailing of steel, concrete, wood, and masonry lateral force resisting elements. The course will build on these analysis and design fundamentals to examine the technical considerations and methodologies for evaluating the lateral force resisting systems of existing, oftentimes monumental, building structures, and for designing and implementing repairs and retrofits to these lateral systems, including the application of Performance Based Design. This course is co-listed with EN.565.636 . Distribution Area: Engineering EN.560.646. Smart Cities. 3 Credits. In recent years, sustainability progress has resulted mainly from developing and implementing smart, sustainable technology solutions. This course examines opportunities to drive sustainability through technology applications, deemed the “smart city”. Smart city technology ranges from intelligent infrastructure in modern cities to mobile applications that enable the “sharing economy” and facilitate energy access in remote regions of East Africa. This course will not only concern “first-world” problems; we will explore the transformative solutions currently driving growth in emerging markets and the developing world. Students will develop the skills to piece together a sustainable, smart city. Distribution Area: Engineering EN.560.647. Probabilistic Graphical Models and Causal Inference for Networked Systems. 3 Credits. Many of the problems in civil and systems engineering, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical model framework provides a unified view for this wide range of problems, enables efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for applying graphical models and causality to model complex networked engineering systems. Distribution Area: Engineering EN.560.648. Energy Systems and Policy. 3 Credits. In this course, you will develop an understanding—and a technically- and socially-deep working knowledge—of our energy technologies, policies, and options. This will include analysis of the different opportunities and impacts of energy systems that exist within and between groups defined by national, regional, household, ethnic, and gender distinctions. Analysis of the range of current and future energy choices will be stressed, as well as the role of energy in determining local environmental conditions and the global climate. EN.560.649. Energy Systems. 3 Credits. This course revolves around the grid integration of renewable energy systems and operations of energy systems with renewables. The main emphasis is on grid level effects of renewable energy, particularly solar and wind power systems, and how these effects can be analyzed using mathematical modeling and modern software tools. The course begins with an introduction to basic power system concepts (transmission/distribution system modeling, power transformers, conventional and renewable generation technologies) along with power flow analysis and optimization. Following that, the course considers applications of optimal power flow and its variants to electricity market operations. An important component of this course is a guided project that will be carried out by students in small groups; each group will choose a real-world energy system to research and analyze and will present their findings at the end of the semester. Prior knowledge of circuits (including operations with complex numbers), linear algebra, calculus, and optimization is helpful, but not required. This course is co-listed with EN.560.449 . Distribution Area: Engineering EN.560.650. Operations Research. 3 Credits. An introduction to operations research and its applications. The course will review the basics of mathematical modelling, linear programming, primal and dual Simplex methods, post-optimization analysis, decomposition methods, and heuristic methods along with sample applications. Recommended course background ( EN.553.291 or AS.110.201 ) and AS.110.109 or equivalent. This course is co-listed with EN.560.450 . Prerequisite(s): Students who have taken EN.560.450 are not eligible to take EN.560.650 . Distribution Area: Engineering EN.560.651. Smart Transportation and Autonomous Vehicles. 3 Credits. This course offers an in-depth exploration of the evolving landscape of smart transportation systems and autonomous vehicle technologies. Students will study the integration of intelligent transportation systems (ITS) and automation, with a focus on the development, challenges, and future of autonomous vehicles (AVs) within open-world traffic settings. Key topics include sensor technologies, data quality control and bias mitigation, data processing algorithms, machine learning, vehicle-to-everything (V2X) communication, human-machine interaction, and ethical considerations in technology deployment. Additionally, the course will cultivate students with advanced data processing skills and the role of data science in transportation, emphasizing data-driven solutions for safety, mobility, and sustainability. By the end of the course, students will have gained a comprehensive understanding of how smart technologies are shaping the future of transportation and the role of AVs in fostering safer, more efficient, and sustainable urban mobility systems. Prerequisite(s): Students who are currently enrolled in, or have already completed EN.560.451 , are not eligible to take EN.560.651 . Distribution Area: Engineering EN.560.652. Scientific Machine Learning for Modeling, Optimization, and Control of Dynamical Systems. 3 Credits. This course offers a scientific machine learning (SciML) approach to the modeling, optimization, and control of dynamical systems. Students will learn to systematically integrate physics-based models and constraints into deep learning architectures, and to leverage data-driven methods for accelerating the solution of large-scale optimization and optimal control problems. Key topics include physics-informed neural networks, learning to optimize, neural differential equations, neural operators, and differentiable control. The course also examines real-world applications of these emerging SciML techniques in domains such as building energy management, networked dynamical systems, and power systems. Emphasis will be placed on practical, hands-on coding exercises and project-based assessments to reinforce theoretical concepts through implementation. Distribution Area: Engineering EN.560.653. An Introduction to Network Modeling. 4 Credits. Many real-world problems can be modeled using network structures, and solved using tools from network theory. For this reason, network modeling plays a critical role in various disciplines ranging from physics and mathematics, to biology and computer science, and almost all areas of social science. This course will provide an introduction to network theory, network flow algorithms, modeling processes on networks and examples of empirical network applications spanning transport, health and energy systems. Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.560.654. Introduction to Machine Learning and Control for Building Energy Systems. 3 Credits. This course introduces the principles of building energy optimization with a focus on the modeling and control of HVAC systems. The curriculum covers HVAC fundamentals, thermodynamics, and heat transfer, progressing to dynamic systems, control theory, and optimization techniques. Key topics include an introduction to system identification, machine learning, and optimal control applied to energy-efficient building management. The course concludes with hands-on coding assignments focusing on implementation of machine learning and control techniques for optimizing the energy efficiency of buildings using high-fidelity simulation frameworks. Students are required to posses prior knowledge in:• Differential calculus• Linear algebra• Optimization Distribution Area: Engineering EN.560.656. Space Systems Cybersecurity. 3 Credits. Our space systems are under attack. Cyberattacks are among the most prevalent threats to space assets. They are often stealthy, inexpensive and highly effective at achieving an adversary’s goal – be it data corruption, IP theft or physical destruction of the satellite. Given space systems are complex, composing ground stations, communications and satellites the surface area of attack is vast and considering the constrained computing capacity of space systems, many traditional security mechanisms are not applicable. This course introduces how an adversary would approach attacking a satellite, opportunities for systems engineers to develop cyber-resilient assets and relevant policies and best practices to support space system cybersecurity. Recommended classes - EP 675.600 and 675.601. Distribution Area: Engineering EN.560.657. System Dynamics. 3 Credits. System dynamics is a versatile analytical framework to understand and tackle problems which involve complex interactions among multiple variables and constraints. This course introduces the basics of systems thinking and system dynamics modeling and analysis. Qualitative and quantitative tools are discussed. Students will learn to identify and formulate system's structure and simulate their behavior using specialized software in order to develop potential intervention strategies. Fields of applications include engineering, climate change, resilience, logistics, public policy analysis, business, and decision-making. Distribution Area: Engineering EN.560.658. Natural Disaster Risk Modeling. 3 Credits. This course provides an in-depth discussion of the simulation of disaster risk on socio-technical systems (from countries to cities). The course covers the algorithmic structure of catastrophe models; modeling of the intensity fields of hurricanes, earthquakes, and floods; methods to develop building and infrastructure vulnerability functions, structure of exposure layers, and estimation of post-disaster injuries and casualties. The students learn to produce basic stochastic catalogs from where risk metrics are calculated. Finally, the risk-reduction policy formulation process is presented using as input the catastrophe model-generated information. The course has a strong real-life application side analyzing World Bank risk reduction projects. Students will gain introductory experience in the use of GIS, Matlab, and R. This course is co-listed with EN.560.458 . Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.560.659. Production Systems Analysis. 3 Credits. Planning for manufacturing and service industries is critical for efficiently utilizing resources to produce cost-effective goods and services. This course delves into the fundamentals, models, and techniques required for planning, controlling, and optimizing the performance of manufacturing systems. The curriculum focuses on the trade-offs between key measures, like costs, cycle time, throughput, capacity, work-in-process, inventory, and variability. The course utilizes analytical approaches (linear programming, simulation, probability, and statistics) and coding (Python). Co-listed EN.560.479. Recommended courses that cover topics on probability and coding. Prerequisite(s): Students who have taken or are enrolled in EN.560.479 OR EN.560.459 OR EN.560.679 are not eligible to take EN.560.659 . Distribution Area: Engineering EN.560.661. Additive Manufacturing and Design. 3 Credits. Additive Manufacturing (AM) removes many geometric constraints imposed by traditional manufacturing processes. Resultingly, systems can be designed to support and improve multiple design objectives, which has the potential to alter the way products are designed. While this allows for the fabrication of more complex and often unprecedented geometries, it also increases the complexity designers face. In addition, engineers must not only understand AM technologies and materials, they must also be able to synthesize its economic and environmental impacts on a manufacturing value chain. Additive Manufacturing and Design will provide an in-depth overview of the most common – and promising – AM technologies, materials, and design methods by including examples from state-of-the-art research. A particular emphasis is placed on Design for Additive Manufacturing (DfAM), where the different topics will converge to fully utilize the newly created design space. Distribution Area: Engineering Writing Intensive EN.560.691. CaSE Graduate Seminar. 1 Credit. Graduate students are expected to register for this course each semester. Both internal and outside speakers are included. The first three meetings are dedicated to presentations by the faculty from the Civil and Systems Engineering Department. EN.560.692. Civil Engineering and Systems Engineering Graduate Seminar. 1 Credit. Seminar series of speakers on various aspects of civil engineering. Different speakers are invited each semester. Full time civil engineering graduate students must enroll in the seminar course every semester unless excused by the Department. EN.560.730. Finite Element Methods. 3 Credits. Variational methods and mathematical foundations, Direct and Iterative solvers, 1-D Problems formulation and boundary conditions, Trusses, 2-D/ 3D Problems, Triangular elements, QUAD4 elements, Higher Order Elements, Element Pathology, Improving Element Convergence, Dynamic Problems. EN.560.733. Thin-walled Members. 3 Credits. The Subject aims to discuss the behavior specific to thin-walled structural members (plated members in general, with a special focus on cold-formed steel members). Classic analytical solutions are presented, as well as numerical methods are discussed and employed (such as the Finite Strip Method and shell Finite Element Method). The main topics are as follows: · Theory of thin plates.· Buckling of thin plates.· Thick plates with shear deformations.· Plates with stiffeners, orthotropic plates.· Design concepts for plate buckling.· The finite strip method.· Buckling of thin-walled members: global, local and distortional buckling.· Design concepts for the buckling of thin-walled cold-formed steel members.· Introduction to shell theories.· Buckling of tubular members.· Further phenomena specific to thin-walled members: web crippling, shear lag, flange curling. Distribution Area: Engineering EN.560.762. Mechanics of Architected Materials. 3 Credits. This upper level graduate course will focus on the linear and nonlinear mechanics of a wide range of architected materials; we aim to cover: linear elastic properties of 2D and 3D cellular solids, micromechanics and homogenization, localization, microscopic and macroscopic instabilities, natural architected materials (bone, wood, nacre), wave propagation in lattices and phononics, mechanical metamaterials, and nanostructured materials (carbon nanotubes pillars, DNA origami). Distribution Area: Engineering EN.560.770. Advanced Finite Element Methods and Multi-Scale Methods. 3 Credits. Addresses advanced topics in various areas of the finite element methodology. Covers a range of topics, viz. element stability and hourglass control, adaptive methods for linear and nonlinear problems, mixed and hybrid element technology, eigen-value problems, multi-scale modeling for composites and polycrystalline materials. Recommended Course Background: EN.530.730 or EN.560.730 EN.560.772. Non-Linear Finite Elements. 3 Credits. This course will discuss state of the art theoretical developments and modeling techniques in nonlinear computational mechanics, for problems with geometric and material nonlinearities. Large deformation of elastic-plastic and visco-plastic materials, contact-friction and other heterogeneous materials like composites and porous materials will be considered. A wide variety of applications in different disciplines, e.g. metal forming, composite materials, polycrystalline materials will be considered. EN.560.826. Graduate Independent Study. 1 - 3 Credits. Students who participate in ongoing research activities may register for this course with permission of their supervising faculty member. This course differs from EN.560.501 and 560.511 in that it includes a weekly research group meeting that students are expected to attend. Research is primarily on the sponsored project. Distribution Area: Engineering EN.560.835. Graduate Research. 3 - 20 Credits. Graduate students pursue research problems with a faculty supervisor. This course will provide a Civil and Systems Engineering graduate-level research experience to those pursuing their graduate degrees (Master’s or doctoral degree) which will help a student engage in research on a specific topic and/or in specific research group under faculty supervision. Prior to course registration, students will submit a research proposal for approval by the research supervisor and the student’s faculty advisor. EN.560.836. Graduate Research. 3 - 20 Credits. Graduate students pursue research problems with a faculty supervisor. This course will provide a Civil and Systems Engineering graduate-level research experience to those pursuing their graduate degrees (Master’s or doctoral degree) which will help a student engage in research on a specific topic and/or in specific research group under faculty supervision. Prior to course registration, students will submit a research proposal for approval by the research supervisor and the student’s faculty advisor.
EN.601 (Computer Science)
http://e-catalogue.jhu.g.sjuku.top/course-descriptions/computer_science_601/
EN.601.104. Computer Ethics. 1 Credit. Students will examine a variety of topics regarding policy, legal, and moral issues related to the computer science profession itself and to the proliferation of computers in all aspects of society, especially in the era of the Internet. The course will cover various general issues related to ethical frameworks and apply those frameworks more specifically to the use of computers and the Internet. The topics will include privacy issues, computer crime, intellectual property law -- specifically copyright and patent issues, globalization, and ethical responsibilities for computer science professionals. Work in the course will consist of weekly assignments on one or more of the readings and a final paper on a topic chosen by the student and approved by the instructor. The ePortfolio tag(s) on this course signify that there are one or more assignments offered in the course that provide students with the opportunity to be assessed for proficiency in completion of the relevant ePortfolio requirement(s). Distribution Area: Humanities EN Foundational Abilities: Writing ePortfolio (FA.1.1eP), Ethical Reflection (FA5) EN.601.124. The Ethics of Artificial Intelligence and Automation. 3 Credits. The expansion of artificial intelligence (AI)-enabled use cases across a broad spectrum of domains has underscored the benefits and risks of AI. This course will address the various ethical considerations engineers need to engage with to build responsible and trustworthy AI-enabled autonomous systems. Topics to be covered include: values-based decision making, ethically aligned design, cultural diversity, safety, bias, AI explainability, privacy, AI regulation, the ethics of synthetic life, and the future of work. Case studies will be utilized to illustrate real-world applications. Students will apply learned material to a group research project on a topic of their choice. The ePortfolio tag(s) on this course signify that there are one or more assignments offered in the course that provide students with the opportunity to be assessed for proficiency in completion of the relevant ePortfolio requirement(s). Distribution Area: Humanities, Engineering AS Foundational Abilities: Science and Data (FA2) EN Foundational Abilities: Ethical Reflection (FA5), Ethical Reflection ePortfolio (FA5eP) EN.601.164. Human and Machine Intelligence Alignment. 3 Credits. The challenge of ensuring that the actions of individuals and systems — whether human or machine — are consistent with shared goals, reflect our values, and promote societal well-being is known as "the alignment problem." Over millennia, humans have developed many coordination and cooperation "technologies" — such as customs, values, norms, laws, organizations, governments, and markets—that partially solve the problem of human intelligence alignment.As we develop and deploy advanced technologies like artificial intelligence, we are similarly concerned that their use is consistent with shared goals, reflect our values, and promote societal well-being. In this course we will explore the parallels between human intelligence alignment and machine intelligence alignment to help engineers and technologists become reflective practitioners who can grapple wisely with the alignment problem broadly understood. The ePortfolio tag(s) on this course signify that there are one or more assignments offered in the course that provide students with the opportunity to be assessed for proficiency in completion of the relevant ePortfolio requirement(s). Distribution Area: Humanities, Engineering EN Foundational Abilities: Writing ePortfolio (FA.1.1eP), Ethical Reflection (FA5), Ethical Reflection ePortfolio (FA5eP) EN.601.220. Intermediate Programming. 4 Credits. This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover low-level programming techniques, as well as object-oriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages. Prerequisite(s): (A grade of C+ is required in EN.500.112 OR EN.500.113 OR EN.500.114 ) OR (AP Computer Science OR EN.500.132 OR EN.500.133 OR EN.500.134 ) Students will not be able to register until their grades are posted for the prerequisite courses. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.226. Data Structures. 4 Credits. This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prerequisite(s): A grade of C+ or better in EN.500.112 OR EN.601.220 OR AP Computer Science OR EN.500.132 . Students can't register until grades for prerequisites are posted. Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.229. Computer System Fundamentals. 3 Credits. This course covers modern computer systems from a software perspective. Topics include binary data representation, machine arithmetic, assembly language, computer architecture, performance optimization, memory hierarchy and cache organization, virtual memory, Unix systems programming, network programming, and concurrency. Hardware and software interactions relevant to computer security are highlighted. Students will gain hands-on experience with these topics in a series of programming assignments. Prerequisite(s): EN.601.220 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.230. Mathematical Foundations for Computer Science. 4 Credits. This course provides an introduction to mathematical reasoning and discrete structures relevant to computer science. Topics include propositional and predicate logic, proof techniques including mathematical induction, sets, relations, functions, recurrences, counting techniques, simple computational models, asymptotic analysis, discrete probability, graphs, trees, and number theory. Prerequisite(s): Student may not enroll if taken EN.601.231 OR EN.601.431 ; EN.500.112 OR EN.500.113 OR EN.500.114 OR EN.500.132 OR EN.500.133 OR EN.500.134 OR EN.601.220 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.231. Automata & Computation Theory. 3 Credits. This course is an introduction to the theory of computing. topics include design of finite state automata, pushdown automata, linear bounded automata, Turing machines and phrase structure grammars; correspondence between automata and grammars; computable functions, decidable and undecidable problems, P and NP problems, NP-completeness, and randomization. Prerequisite(s): Students may not enroll if taken EN.601.230 .;EN.550.171/ EN.553.171 OR EN.553.172 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.257. Computer Graphics and 3D Game Programming. 3 Credits. In this course, students will program a game of their own design using an off-the-shelf game engine while learning about the 3D computer graphics concepts behind the engine's components. Classes will consist of a mix of theory and practice. The theory will be presented through lectures on topics including transformations, lighting, shading, shape representations, spatial querying and indexing, animation, and special effects. Practice will involve in-class programming exercises and contributions to the game project with periodic in-class presentations of progress to date. Students are expected to have a strong programming background and to be familiar with basic linear algebra concepts. Prerequisite(s): EN.601.220 AND EN.601.226 AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) Distribution Area: Engineering EN.601.264. Practical Generative AI. 3 Credits. This course is a comprehensive guide for students eager to explore the world of generative AI and its practical applications in software development. Designed with a hands-on approach, it equips you with the foundational knowledge of generative AI, introduces a suite of AI development tools, and covers key AI platforms and frameworks. You'll gain the skills needed to build and deploy AI-powered applications, culminating in a substantial team project that offers real-world experience in creating AI-driven software. By the end of the course, you'll be prepared to integrate AI into your applications and development process, unlocking new avenues for creativity and innovation. Recommended course background: EN.601.290 or EN.601.490 . Prerequisite(s): Students who have previously taken, or are currently enrolled in, EN.601.470 OR EN.601.670 OR EN.601.471 OR EN.601.671 are not eligible to take EN.601.264 .; EN.601.220 AND EN.601.226 AND EN.601.280 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.270. Open Source Software Engineering (Semesters of Code I). 3 Credits. The course will provide students a development experience focused on learning software engineering skills to deliver software at scale to a broad community of users associated with open source licensed projects. The class work will introduce students to ideas behind open source software with structured modules on recognizing and building healthy project structure, intellectual property basics, community & project governance, social and ethical concerns, and software economics. Prerequisite(s): EN.601.220 AND EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.277. Disinformation Self-Defense. 3 Credits. Scientific, statistical and logical literacy is a necessary skill for evaluating policy proposals, reading news articles with an appropriately critical eye, and making informed choices as consumers and voters. Misunderstanding of claims made in scientific publications, online publishing platforms, and mass media drives, in part, the spread of malicious misinformation and propaganda online. Further, many actors have the means, the motive and the opportunity to mislead the public in a variety of subtle and not so subtle ways. This class will give you tools to discern valid and invalid forms of inference and discourse, and give you tools to communicate precisely, argue appropriately, and stay on top of research and news with an appropriately skeptical attitude. A use case used throughout the class will be online disinformation surrounding the COVID-19 pandemic. The class will draw on historical and modern literature on linguistic, logical, and probabilistic fallacies, statistical and logical inference, data visualization, cognitive biases, and the scientific method. Prerequisite(s): EN.601.230 OR EN.553.171 OR EN.553.172 OR AS.150.118 Distribution Area: Quantitative and Mathematical Sciences, Social and Behavioral Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.280. Full-Stack JavaScript. 3 Credits. A full-stack JavaScript developer is a person who can build modern software applications using primarily the JavaScript programming language. Creating a modern software application involves integrating many technologies - from creating the user interface to saving information in a database and everything else in between and beyond. A full-stack developer is not an expert in everything. Rather, they are someone who is familiar with various (software application) frameworks and the ability to take a concept and turn it into a finished product. This course will teach you programming in JavaScript and introduce you to several JavaScript frameworks that would enable you to build modern web, cross-platform desktop, and native/hybrid mobile applications. A student who successfully completes this course will be on the expedited path to becoming a full-stack JavaScript developer. Prerequisite(s): Students must not have taken or be concurrently enrolled in EN.601.421 or EN.601.621 Object Oriented Software Engineering.; EN.601.220 OR EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.290. User Interfaces and Mobile Applications. 3 Credits. This course will provide students with a rich development experience, focused on the design and implementation of user interfaces and mobile applications. A brief overview of human computer interaction will provide context for designing, prototyping and evaluating user interfaces. Students will invent their own mobile applications and implement them using the Android SDK, which is JAVA based. An overview of the Android platform and available technologies will be provided, as well as XML for layouts, and general concepts for effective mobile development. Students will be expected to explore and experiment with outside resources in order to learn technical details independently. There will also be an emphasis on building teamwork skills, and on using modern development techniques and tools. Prerequisite(s): EN.601.220 AND EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.315. Databases. 3 Credits. Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. Prerequisite(s): Students can only take one of the following: EN.601.315 , EN.601.415 , OR EN.601.615 .; EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.318. Operating Systems. 3 Credits. This course covers fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. Prerequisite(s): Students may receive credit for only one of the following: EN.601.318 , EN.601.418 , OR EN.601.618 .; EN.601.226 AND EN.601.229 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.340. Web Security. 3 Credits. This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed.Note: This undergrad version will not have the same paper component as the other versions of this course. Prerequisite(s): Students may receive credit for only one of 601.340/440/640.; EN.601.226 AND EN.601.229 AND EN.601.280 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.350. Genomic Data Science. 3 Credits. This course will use a project-based approach to introduce undergraduates to research in computational biology and genomics. During the semester, students will take a series of large data sets, all derived from recent research, and learn all the computational steps required to convert raw data into a polished analysis. Data challenges might include the DNA sequences from a bacterial genome project, the RNA sequences from an experiment to measure gene expression, the DNA from a human microbiome sequencing experiment, and others. Topics may vary from year to year. In addition to computational data analysis, students will learn to do critical reading of the scientific iterature by reading high-profile research papers that generated groundbreaking or controversial results. Recommended Course Background: Knowledge of the Unix operating system and programming expertise in a language such as Perl or Python. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.356. Seminar: Computer Integrated Surgery II. 1 Credit. Students may receive credit for EN.601.456 or EN.601.356 , but not both. Lecture only version of EN.601.456 (no project). Recommended Course Background: EN.601.455 or instructor permission required. Prerequisite(s): EN.601.455 or instructor permission.;Students may receive credit for either EN.601.356 orEN.601.456, but not both. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.402. Digital Health and Biomedical Informatics. 1 Credit. Advances in technology are driving a change in medicine, from personalized medicine to population health. Computers and information technology will be critical to this transition. We shall discuss some of the coming changes in terms of computer technology, including computer-based patient records, clinical practice guidelines, and region-wide health information exchanges. We will discuss the underlying technologies driving these developments - databases and warehouses, controlled vocabularies, and decision support. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.404. Brain & Computation. 1 Credit. Computational and network aspects of the brain are explored. The topics covered include structure, operation and connectivity of neurons, general network structure of the neural system, and the connectivity constraints imposed by pre- and post-natal neural development and the desirability of network consistency within a species. Both discrete and continuous aspects of neural computation are covered. Precise mathematical tools and analyses such as logic design, transient and steady state behavior of linear systems, and time and connectivity randomization are discussed. The concepts are illustrated with several applications. Memory formation from the synaptic level to the high level constructs are explored. Students are not expected to master any of the mathematical techniques but are expected to develop a strong qualitative appreciation of their power. Cerebellum, which has a simple network connectivity, will be covered as a typical system. Prerequisite(s): Students can receive credit for EN.601.404 or EN.601.604 , but not both;( EN.553.291 OR (( AS.110.201 OR AS.110.212 ) AND AS.110.302 )) AND ( EN.553.420 OR EN.553.421 OR EN.553.211 OR EN.553.310 OR EN.553.311 ) AND EN.601.433 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.411. Computer Science Innovation & Entrepreneurship II. 3 Credits. This course is the second half of a two-course sequence and is a continuation of course EN.660.410 .01, CS Innovation and Entrepreneurship, offered by the Center for Leadership Education (CLE). In this sequel course the student groups, directed by CS faculty, will implement the business idea which was developed in the first course and will present the implementations and business plans to an outside panel made up of practitioners, industry representatives, and venture capitalists. Prerequisite(s): EN.660.410 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.413. Software Defined Networks. 3 Credits. Software-Defined Networks (SDN) enable programmability of data networks and hence rapid introduction of new services. They use software-based controllers to communicate with underlying hardware infrastructure and direct traffic on a network. This model differs from that of traditional networks, which use dedicated hardware devices (i.e., routers and switches) to control network traffic. This technology is becoming a key part of web scale networks (at companies like Google and Amazon) and 5G/6G networks. Its importance will keep on growing. Many of today’s services and applications, especially when they involve the cloud, could not function without SDN. SDN allows data to move easily between distributed locations, which is critical for cloud applications. A major focus will be on how this technology will be used in 5G and 6G Networks. The course will cover basics of SDN, ongoing research in this area, and the industrial deployments. Prerequisite(s): Students can receive credit for EN.601.413 or EN.601.613 , but not both; EN.601.414 OR EN.601.614 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.414. Computer Networks. 3 Credits. Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. Prerequisite(s): EN.601.226 AND EN.601.229 or permission.;Students may receive credit for only one of EN.600.344, EN.600.444, EN.601.414 , EN.601.614 . Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.415. Databases. 3 Credits. Similar material as EN.601.315 covered in more depth for advanced undergraduates. Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. (www.cs.jhu.g.sjuku.top/~yarowsky/cs415.html) Prerequisite(s): Students may receive credit for only one of the following: EN.601.315 , EN.601.415 , OR EN.601.615 .; EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.418. Operating Systems. 3 Credits. Similar material as EN.601.318 , covered in more depth. Intended for advanced undergraduate students. This course covers fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. Prerequisite(s): Students may receive credit for only one of the following: EN.601.318 , EN.601.418 , OR EN.601.618 .; EN.601.226 AND EN.601.229 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.419. Cloud Computing. 3 Credits. Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and low-latency requirements of real-time applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming. Prerequisite(s): Students may earn credit for EN.601.419 or EN.601.619 , but not both.; EN.601.226 (or EN.600.226) AND EN.601.414 or permission from the instructor. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.420. Parallel Computing for Data Science. 3 Credits. This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. The course will not cover GPU deep-learning frameworks nor CUDA. The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience and familiarity with Python. Prerequisite(s): EN.601.226 AND EN.601.229 ;Students may receive credit for only one of EN.600.320, EN.600.420, EN.601.320, EN.601.420 , EN.601.620 . Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.421. Object Oriented Software Engineering. 3 Credits. This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. Prerequisite(s): Students may receive credit for only one of EN.600.321, EN.600.421, EN.601.421 , EN.601.621 .; EN.601.220 AND EN.601.226 AND ( EN.601.280 OR EN.601.290 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.422. Software Testing & Debugging. 3 Credits. Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. Prerequisite(s): EN.601.290 OR EN.601.421 ;Students can take EN.601.422 or EN.601.622 , but not both. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.424. Reliable Software Systems. 3 Credits. Reliability is an essential quality requirement for all artifacts operating in the real-world, ranging from bridges, cars to power grids. Software systems are no exception. In this computing age when software is transforming even traditional mission-critical artifacts, making sure the software we write is reliable becomes ever more important. This course exposes students to the principles and techniques in building reliable systems. We will study a set of systematic approaches to make software more robust. These include but are not limited to static analysis, testing framework, model checking, symbolic execution, fuzzing, and formal verification. In addition, we will cover the latest research in system reliability. Prerequisite(s): EN.601.220 AND (EN.601.328 OR EN.601.428 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.425. Software System Design. 3 Credits. This course introduces modern software systems design, with an emphasis on how to design large-scale systems, assess common system design trade-offs, and tackle system design challenges. It covers non-functional requirements, API design, distributed systems concepts, modern software building blocks (e.g., load balancers, caches, containers, etc.). Additionally, it includes case studies of common system design problems, some drawn from interview questions. Ultimately, this course helps learners become better software engineers. Prerequisite(s): Students may receive credit for EN.601.425 OR EN.601.625 , but not both.; EN.601.315 OR EN.601.415 OR EN.601.615 OR EN.601.280 OR EN.601.290 OR EN.601.340 OR EN.601.440 OR EN.601.640 OR EN.601.421 OR EN.601.621 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.426. Principles of Programming Languages. 3 Credits. Functional, object-oriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. Prerequisites include EN.601.226 . No Freshmen or Sophomores. Prerequisite(s): Students can receive credit for EN.601.426 or EN.601.626 , but not both; EN.601.226 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.428. Compilers & Interpreters. 3 Credits. Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. Recommended background: EN.601.230 or EN.601.231 . Prerequisite(s): EN.601.226 AND EN.601.229 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.429. Functional Programming in Software Engineering. 3 Credits. How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project. Prerequisite(s): Students can receive credit for EN.601.429 or EN.601.629 , but not both.; EN.601.226 OR Instructor Permission Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.430. Combinatorics & Graph Theory in Computer Science. 3 Credits. This course covers the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. Prerequisite(s): EN.601.230 OR EN.553.171 OR EN.553.172 OR EN.550.171;Students may receive credit for EN.601.430 or EN.601.630 , but not both. Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.431. Theory of Computation. 3 Credits. This course covers the theoretical foundations of computer science. Topics included will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.431/601.631, unless one is for an undergrad degree and the other for grad. Prerequisite(s): Students who have taken EN.601.631 OR EN.601.231 are not eligible to take EN.601.431 .; EN.553.171 OR EN.553.172 OR EN.601.230 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.433. Intro Algorithms. 3 Credits. This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. Prerequisite(s): EN.601.226 AND ( EN.553.171 OR EN.553.172 OR EN.601.230 OR EN.601.231 );Students may receive credit for only one of EN.600.363, EN.600.463, EN.601.433 , EN.601.633 . Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.434. Randomized and Big Data Algorithms. 3 Credits. This course will cover fundamental methods of randomization for efficient algorithms and relevant techniques in probabilistic analysis. The first part of the course will discuss classical randomized algorithms, including the randomized algorithm for the min-cut problem, hashing techniques, tail inequalities, and the probabilistic method. The second part will delve into advanced topics such as coreset methods for clustering algorithms, the Johnson-Lindenstrauss lemma, and the applications of streaming and sketching algorithms. Prerequisite(s): Students may receive credit for only one of the following: EN.601.434 OR EN.601.634 .;( EN.601.433 OR EN.601.633 ) AND ( EN.553.211 OR EN.553.310 OR EN.553.311 OR EN.553.420 OR EN.553.421 ) Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.435. Approximation Algorithms. 3 Credits. This course provides an introduction to approximation algorithms. Topics include vertex cover, TSP, Steiner trees, cuts, greedy approach, linear and semi-definite programming, primal-dual method, and randomization. Additional topics will be covered as time permits. There will be a final project. Students may receive credit for EN.601.435 or EN.601.635 , but not both. Prerequisite(s): Students can only receive credit for one of EN.601.435 or EN.601.635 .;EN.600.363 OR EN.601.433 OR EN.601.633 OR permission. Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.436. Algorithmic Game Theory. 3 Credits. This course provides an introduction to algorithmic game theory: the study of games from the perspective of algorithms and theoretical computer science. There will be a particular focus on games that arise naturally from economic interactions involving computer systems (such as economic interactions between large-scale networks, online advertising markets, etc.), but there will also be broad coverage of games and mechanisms of all sorts. Topics covered will include a) complexity of computing equilibria and algorithms for doing so, b) (in)efficiency of equilibria, and c) algorithmic mechanism design. Prerequisite(s): EN.600.363 OR EN.600.463 OR EN.601.433 OR EN.601.633 Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.438. Algorithmic Foundations of Differential Privacy. 3 Credits. This course provides an introduction to differential privacy, with a focus on algorithmic aspects (rather than statistical or engineering aspects). Specific topics we will cover include: motivation for differential privacy, and different versions of differential privacy (pure, approximate, Renyi, and zero-concentrated in particular); basic mechanisms (Laplace, Gaussian, Discrete Gaussian, and Exponential); composition theorems; basic algorithmic techniques (sparse vector technique, private multiplicative weights, private selection); beyond global sensitivity: local sensitivity, propose-test-release, subsampling; differentially private graph algorithms; lower bounds. Prerequisite(s): Students may earn credit for EN.601.438 or EN.601.638 , but not both.; EN.601.433 OR EN.601.633 Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.440. Web Security. 3 Credits. This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. Prerequisite(s): Students may receive credit for only one of 340/440/640.; EN.601.226 AND EN.601.229 AND EN.601.280 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.441. Blockchains and Cryptocurrencies. 3 Credits. This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. Prerequisite(s): Students may recieve credit for only one of EN.600.451 OR EN.601.441 OR EN.601.641 ; EN.601.226 AND ( EN.553.211 OR EN.553.310 OR EN.553.311 OR EN.560.348 OR EN.553.420 OR EN.553.421 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.442. Modern Cryptography. 3 Credits. Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zero-knowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. Prerequisite(s): Students may receive credit for only one of EN.600.442, EN.601.442 , EN.601.642 .;( EN.601.230 OR EN.601.231 ) AND ( EN.553.310 OR EN.553.311 OR EN.553.420 OR EN.553.421 ) Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.443. Security & Privacy in Computing. 3 Credits. Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. Prerequisite(s): EN.601.229 (Computer System Fundamentals);Students may receive credit for only one of EN.600.443, EN.601.443 , EN.601.643 . Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.444. Medical Device Cybersecurity. 3 Credits. In an increasingly connected healthcare landscape, medical devices have effectively become IT endpoints, often running general-purpose operating systems like Windows or Linux, incorporating cloud microservices, and integrating artificial intelligence to detect, prevent, and improve patient health outcomes. Protecting these devices from cyber threats is not just a technical challenge—it's a matter of patient safety. A security breach in medical devices like pacemakers or infusion pumps can have life-threatening consequences. National and international regulatory bodies, such as the FDA and EU National Competent Authorities (NCAs) and Medical Device Regulation (MDR), know the implications and have provided prescription and guidance emphasizing stringent cybersecurity measures throughout a device's lifecycle, from design and development to postmarket surveillance. The result is a heightened awareness of medical device security and its impact on healthcare delivery, requiring cybersecurity risk management. In particular, focusing on threat modeling, cybersecurity risk assessment, secure design, secure coding practices, vulnerability management and monitoring, software bill of materials, cybersecurity transparency, user labeling, penetration testing, and more. Recommended background: computing systems, operating systems, machine learning & AI. Prerequisite(s): Students who have taken, or are currently enrolled in EN.601.644 are not eligible to enroll in EN.601.444 ; EN.601.443 OR EN.601.643 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.445. Practical Cryptographic Systems. 3 Credits. This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem. We will place a particular emphasis on the techniques of provable security and the feasibility of reverse-engineering undocumented cryptographic systems. Prerequisite(s): Students may receive credit for only one of EN.601.445 OR EN.601.645 , but not both.; EN.601.226 AND EN.601.229 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.446. Sketching and Indexing for Sequences. 3 Credits. Many of the world's largest and fastest-growing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing --- succinct and compressed indexes in particular -- which enables us to efficiently search inside very long strings, especially in highly repetitive texts. Prerequisite(s): Students can receive credit for EN.601.446 or EN.601.646 , but not both; EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.447. Computational Genomics: Sequences. 3 Credits. Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming projects. Prerequisite(s): Students may receive credit for only one of the following: EN.601.447 OR EN.601.647 but not both.; EN.601.220 AND EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.449. Computational Genomics: Applied Comparative Genomics. 3 Credits. The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. Prerequisites: knowledge of the Unix operating system and programming expertise in a language such as R or Python. Prerequisite(s): Students may receive credit for only one of EN.600.449, EN.600.649, EN.601.749. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.451. Introduction to Computational Immunogenomics. 3 Credits. Immunology studies defensive mechanisms of living organisms against external threats. Computational immunogenomics is a new field of bioinformatics that develops and applies computational approaches to the study and interpretation of immunological data, seeking to answer questions about adaptive immune responses in humans and important animals. In this course, students will learn how to design, apply, and benchmark algorithms for solving immunogenomics problems. Students may receive credit for only one of EN.601.451 , EN.601.651 . Prerequisite(s): Students may receive credit for only one of EN.601.451 OR EN.601.651 .; EN.601.220 AND EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.453. Applications of Augmented Reality. 3 Credits. This course is designed to expand the student’s augmented reality knowledge and introduce relevant topics necessary for developing more meaningful applications and conducting research in this field. The course addresses the fundamental concepts of visual perception and introduces non-visual augmented reality modalities, including auditory, tactile, gustatory, and olfactory applications. The following sessions discuss the importance of integrating user-centered design concepts to design meaningful augmented reality applications. A later module introduces the basic requirements to design and conduct user studies and guidelines on interpreting and evaluating the results from the studies. During the course, students conceptualize, design, implement and evaluate the performance of augmented reality solutions for their use in industrial applications, teaching and training, or healthcare settings. Homework in this course will relate to applying the theoretical methods used for designing, implementing, and evaluating augmented reality applications. Prerequisite(s): Students may receive credit for only one of EN.601.453 or EN.601.653 , but not both.; EN.601.454 OR EN.601.654 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.454. Introduction to Augmented Reality. 3 Credits. This course introduces students to the field of Augmented Reality. It reviews its basic definitions, principles, and applications. The course explains how fundamentals concepts of computer vision are applied for the development of Augmented Reality applications. It then focuses on describing the principal components and particular requirements to implement a solution using this technology. The course also discusses the main issues of calibration, tracking, multi-modal registration, advanced visualization, and display technologies. Homework in this course will relate to the mathematical methods used for calibration, tracking, and visualization in augmented reality. Prerequisite(s): Students may receive credit for only one of the following: EN.601.454 OR EN.601.654 , but not both.; EN.601.220 AND EN.601.226 AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.455. Computer Integrated Surgery I. 4 Credits. This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. Recommended Course Background: EN.601.220 , EN.601.457 , EN.601.461 , image processing. Prerequisite(s): Students may receive credit for only one of EN.600.445, EN.600.645, EN.601.455 , EN.601.655 .;EN.600.226/ EN.601.226 AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) or permission of the instructor. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.456. Computer Integrated Surgery II. 3 Credits. This weekly lecture/seminar course addresses similar material to EN.601.455 , but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from EN.601.455 , although it does not have to be. Grades are based both on the project and on classroom recitations. Students who wish to use this course to satisfy the "Team" requirement should register for EN.601.496 instead. Students wishing to attend the weekly lectures as a 1-credit seminar should sign up for EN.601.356 . Prerequisite(s): Students may receive credit for only one of EN.600.446, EN.600.646, EN.601.456 , EN.601.496 , OR EN.601.656 .; EN.601.455 or EN.601.655 or permisssion Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.457. Computer Graphics. 3 Credits. This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. Prerequisite(s): Students may receive credit for only one of the following: EN.601.457 OR EN.601.657 , but not both.; EN.601.220 AND EN.601.226 AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.461. Computer Vision. 3 Credits. This course provides an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3-D geometry from binocular stereo, motion, and photometric stereo, and objectrecognition, image segmentation, and activity analysis. Elements of machine vision and biological vision are also included. Prerequisite(s): Students may receive credit for only one of EN.600.361, EN.600.461, EN.600.661, EN.601.461 , EN.601.661 .;( EN.553.310 OR EN.553.311 OR (( EN.553.420 OR EN.553.421 ) AND ( EN.553.430 OR EN.553.431 )) AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 )) AND ( EN.500.112 OR EN.500.113 OR EN.500.114 OR EN.601.220 AS.250.205 ) Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.463. Algorithms for Sensor-Based Robotics. 3 Credits. This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. Prerequisite(s): Students may receive credit for only one of EN.600.336, EN.600.436, EN.600.636, EN.601.463 , EN.601.663 .;( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) AND ( AS.110.202 OR AS.110.211 ) AND EN.601.226 AND ( EN.553.211 OR EN.553.310 OR EN.553.311 OR EN.553.420 OR EN.553.421 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.464. Artificial Intelligence. 3 Credits. The course situates the study of Artificial Intelligence (AI) first in the broader context of Cognitive Science (i.e., human intelligence) and then treats in-depth principles and methods for reasoning, planning, and learning, including both conventional methods and recent deep learning approaches. The class is recommended for all scientists and engineers with a genuine curiosity about how to build an AI system (in particular, an intelligent agent) that can learn, reason about, and interact with the world and other agents. Strong programming skills and a solid mathematical foundation are expected. Students will be asked to complete both programming assignments and writing assignments. Students can only receive credit for one of 601.464/664. Prerequisite(s): EN.600.226/ EN.601.226 ;Students may receive credit for only one of EN.600.335, EN.600.435, EN.601.464 , EN.601.664 . Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.465. Natural Language Processing. 4 Credits. An in-depth introduction to core techniques for analyzing, transforming, and generating human language. The course spans linguistics, modeling, algorithms, and applications. (1) How should linguistic structure and meaning be represented (e.g., trees, morphemes, ?-terms, vectors)? (2) How can we formally model the legal structures and their probabilities (e.g., grammars, automata, features, log-linear models, recurrent neural nets, Transformers)? (3) What algorithms can estimate the parameters of these models (e.g., gradient descent, EM) and efficiently identify probable structures (e.g., dynamic programming, beam search)? (4) Finally, what kinds of systems can be built with these techniques and how are they constructed and evaluated in practice? Detailed assignments guide students through many details of implementing core NLP methods. The course proceeds from first principles, although prior exposure to AI, statistics, ML, or linguistics can be helpful. Prerequisite: Data Structures and basic familiarity with Python, partial derivatives, matrix multiplication and probabilities. Prerequisite(s): EN.600.226/ EN.601.226 ;Students may receive credit for only one of EN.600.465, EN.601.465 , EN.601.665 . Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.466. Information Retrieval and Web Agents. 3 Credits. An in-depth, hands-on study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. Recommended Course Background: EN.601.226 Prerequisite(s): Students can receive credit for EN.601.466 or EN.601.666 , but not both;EN.600.226 OR EN.601.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.467. Introduction to Human Language Technology. 3 Credits. This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; informationextraction; knowledge representation; machine translation; dialog systems; etc. Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Prerequisite(s): Students can receive credit for only one of 601.467/601.647; EN.601.226 OR EN.600.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.468. Machine Translation. 3 Credits. Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. Prerequisite(s): Students may receive credit for only one of EN.600.468, EN.601.468 , EN.601.668 .; EN.601.226 AND EN.553.211 OR EN.553.310 OR EN.553.311 OR (( EN.553.420 OR EN.553.421 ) AND ( EN.553.430 OR EN.553.431 ))) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.469. AI Safety, Alignment, & Governance. 3 Credits. This course will focus on the alignment and governance challenges posed by advanced frontier/general purpose AI models: why these models may behave in ways that pose significant risk to human welfare and what technical and governance approaches might mitigate these risks. We’ll begin the course studying general results from alignment and governance in human normative systems such as markets, politics, norms and laws. We’ll pay special attention to risks arising from agentic AI. We’ll then look at current technical and position papers in various topics in AI safety and alignment. Topics could include: RLHF, constitutional AI, red-teaming, safety evaluation methods, red lines, jail-breaking, prompt injection, over-optimization, and open-source debates. We’ll conclude with discussion of regulatory frameworks such as regulatory markets, registration of frontier models, international governance organizations, registration of AI agents and legal personhood for AI agents. This is a paper-reading class. Prerequisite(s): Students may only receive credit for EN.601.469 OR EN.601.669 .;En.601.474 OR En.601.674 OR En.601.475 OR En.601.675 OR En.601.482 OR En.601.682 OR En.601.486 OR En.601.686 Distribution Area: Engineering EN.601.470. Artificial Agents. 3 Credits. This course covers a number of topics explored in introductory AI, such as knowledge representation, reasoning, and natural language understanding. Unlike introductory AI, we will pursue these topics based on the transformer neural architecture. We will motivate the material through interacting with agents in games: how to build models that understand user commands, how to generate responses back to a user, and how to reason about a synthetic environment to determine a course of action. Assignments will include programming, presentations on readings, and written summaries of readings. Prerequisite(s): ( EN.601.475 OR EN.601.675 ) OR ( EN.601.482 OR EN.601.682 ) OR (EN.601.488 OR EN.601.688) OR ( EN.601.486 OR EN.601.686 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.471. Natural Language Processing: Self-Supervised Models. 3 Credits. The rise of massive self-supervised (pre-trained) models have transformed various data-driven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to self-supervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own self-supervised neural network models, using the Pytorch framework. Students should have familiarity with Python/PyTorch. Prerequisite(s): Students may receive credit for EN.601.471 or EN.601.671 , but not both.; EN.601.226 AND ( EN.553.211 OR EN.553.310 OR EN.553.311 OR EN.553.420 OR EN.553.421 ) AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.472. Natural Language Processing for Computational Social Science. 3 Credits. [Alt. title: Analyzing Text as Data] Vastly available digitized text data has created new opportunities for understanding social phenomena. Relatedly, social issues like toxicity, discrimination, and propaganda frequently manifest in text, making text analyses critical for understanding and mitigating them. In this course, we will centrally explore: how can we use NLP as a tool for understanding society? Students will learn core and recent advances in text-analysis methodology, building from word-level metrics to embeddings and language models as well as incorporating statistical methods such as time series analyses and causal inference. Students may receive credit for EN.601.472 or EN.601.672 , but not both. Prerequisite(s): Students may receive credit for only one of the following: EN.601.472 OR EN.601.672 ;(( EN.601.465 OR EN.601.665 ) OR ( EN.601.467 OR EN.601.667 ) OR ( EN.601.468 OR EN.601.668 )) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.473. Cognitive Artificial Intelligence. 3 Credits. Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment. Strongly recommended: a prior course in machine learning or artificial intelligence. Prerequisite(s): Students who have taken EN.601.673 are not eligible to enroll in EN.601.473 .;(((( EN.553.420 OR EN.553.421 ) AND ( EN.553.430 OR EN.553.431 )) OR ( EN.553.211 OR EN.553.310 OR EN.553.311 ) AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) AND ( EN.500.112 OR EN.500.113 OR EN.500.114 OR EN.601.220 OR AS.250.205 ))) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.474. ML: Learning Theory. 3 Credits. This machine learning course will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. Prerequisite(s): ( AS.110.202 OR AS.110.211 ) AND ((( EN.553.420 OR EN.553.421 ) AND ( EN.553.430 OR EN.553.431 )) OR ( EN.553.211 OR EN.553.310 OR EN.553.311 ) AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 )) AND ( EN.500.112 OR EN.500.113 OR EN.500.114 ) OR ( EN.601.220 OR AS.250.205 OR EN.580.200 OR EN.601.107) Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.475. Machine Learning. 3 Credits. Machine learning is subfield of computer science and artificial intelligence, whose goal is to develop computational systems, methods, and algorithms that can learn from data to improve their performance. This course introduces the foundational concepts of modern Machine Learning, including core principles, popular algorithms and modeling platforms. This will include both supervised learning, which includes popular algorithms like SVMs, logistic regression, boosting and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include a heavy programming components, requiring students to implement several machine learning algorithms in a common learning framework. Additionally, analytical homework questions will explore various machine learning concepts, building on the pre-requisites that include probability, linear algebra, multi-variate calculus and basic optimization. Students in the course will develop a learning system for a final project. Prerequisite(s): Students may receive credit for only one of EN.600.475, EN.601.475 , EN.601.675 .;Linear Algebra, Probability, Statistics, Calc III, and Intro Computing/Programming - ( AS.110.202 OR AS.110.211 ) AND ( EN.553.211 OR EN.553.310 OR EN.553.311 OR (( EN.553.420 or EN.553.421 ) AND ( EN.553.430 OR EN.553.431 )) AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) AND ( EN.500.112 OR EN.500.113 OR EN.500.114 OR ( EN.601.220 OR EN.600.120) OR AS.250.205 OR EN.580.200 OR (EN.600.107 OR EN.601.107)). Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.477. Causal Inference. 3 Credits. "Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Prerequisite(s): Students may receive credit for only one of EN.600.477, EN.600.677, EN.601.477 , EN.601.677 .; EN.601.475 OR ( EN.553.211 OR EN.553.311 OR EN.553.420 OR EN.553.421 ) AND ( AS.110.202 OR AS.110.211 ) or permission of instructor. Distribution Area: Engineering, Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.479. Machine Learning: Reinforcement Learning. 3 Credits. Tremendous success of reinforcement learning (RL) in a variety of settings from AlphaGo to LLMs makes it a critical area to study. This course will study classical aspects of RL as well as its modern counterparts. Topics will include Markov Decision Processes, dynamic programming, model-based and model-free RL, temporal difference learning, Monte Carlo methods, multi-armed bandits, policy optimization and other methods. Prerequisite(s): Students who have taken, or are currently enrolled in EN.601.679 , are not eligible to take EN.601.479 .;((( EN.601.464 OR EN.601.664 ) OR ( EN.601.475 OR EN.601.675 ) OR ( EN.601.482 OR EN.601.682 )) AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) AND ( EN.553.211 OR EN.553.310 OR EN.553.311 OR EN.553.420 OR EN.553.421 )) Distribution Area: Engineering EN.601.482. Machine Learning: Deep Learning. 4 Credits. Deep learning (DL) has emerged as a powerful tool for solving data-intensive learning problems such as supervised learning for classification or regression, dimensionality reduction, and control. As such, it has a broad range of applications including speech and text understanding, computer vision, medical imaging, and perception-based robotics. The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics. Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. Strongly recommended courses: Python, Machine Learning, Statistics, Calc III Prerequisite(s): Students can receive credit for EN.601.482 or EN.601.682 , but not both; EN.601.226 AND ( AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295 ) AND ( EN.553.211 OR EN.553.310 OR EN.553.311 OR EN.553.420 OR EN.553.421 ) AND ( AS.110.107 OR AS.110.109 OR AS.110.113 ); Strongly recommended courses: Python, Machine Learning, Statistics, Calc III Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.484. Explainable AI Design & Human-AI Interaction. 3 Credits. This is a design course. Increasing the trustworthiness of machine learning solutions has emerged as an important research area. One approach to trustworthy machine learning is explainable and/or interpretable machine learning, which attempts to reveal the working mechanisms of a machine learning system. However, other than on-task performance, explainability is not a property of machine learning algorithms, but an affordance: a relationship between explanation model and the target users in their context. Successful development of machine learning solutions that afford explainability thus requires understanding of techniques beyond pure machine learning. In this course, we will first review the basics of machine learning and human-centered design. Then, we will introduce several techniques to explain machine learning models and/or make them interpretable, and through hands-on sessions and case studies, will investigate how these techniques affect human-AI interaction.In addition to individual homework assignments, students will work in groups to design, justify, implement, and test an explainable machine learning algorithm for a problem of their choosing.Additional Recommended (601.454/654, 601.290, 601.490/690 or 601.491/691) and 601.477/677. Prerequisite(s): Students may receive credit for EN.601.484 or EN.601.684 , but not both.;(EN.601.476 OR EN.601.676) OR ( EN.601.464 OR EN.601.664 ) OR ( EN.601.482 OR EN.601.682 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.485. Probabilistic Models of the Visual Cortex. 3 Credits. The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. Programming experience (Python preferred). Prerequisite(s): Students who have taken AS.050.375 / AS.050.675 / EN.601.685 are not eligible to take EN.601.485 .; AS.110.106 OR AS.110.108 Distribution Area: Quantitative and Mathematical Sciences AS Foundational Abilities: Science and Data (FA2) EN.601.486. Machine Learning: Artificial Intelligence System Design & Development. 3 Credits. Advances in Artificial intelligence have opened new opportunities for developing systems to aid in numerous areas of society. In order for AI systems to succeed in making constructive and positive changes, we must consider their impact on everyday life. Specifically, AI system designers must evaluate the overall capabilities of the system, consider the resulting human-AI interactions, and ensure that the system behaves in a responsible and ethical manner. In this project-based course you will work in teams of 3-5 students to 1) Identify a need with high-impact implications on everyday life; 2) Articulate principles of Responsible AI relevant to the intended application, 3) Conceptualize and design an AI system targeting this need, and 4) Develop the AI system by refining a demo-able prototype based on feedback received during course presentations. Additionally, we will discuss potential ethical issues that can arise in AI and how to develop Responsible AI principles. Coursework will consist of writing assignments, project presentations, and a project demonstration. Recommended background: Python programming, EN.601.290 or EN.601.454 /654 or EN.601.490 /690 or EN.601.491 /691. Prerequisite(s): ( EN.601.475 OR EN.601.675 ) OR ( EN.601.464 OR EN.601.664 ) OR ( EN.601.482 OR EN.601.682 ) Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.487. Machine Learning: Coping with Non-Stationary Environments and Errors. 3 Credits. This course teaches machine learning methods that 1) consider data distribution shift and 2) represent and quantify the model uncertainty in a principled way. The topics we will cover include machine learning techniques that deal with data distribution shift, including domain adaptation, domain generalization, and distributionally robust learning techniques, and various uncertainty quantification methods, including Bayesian methods, conformal prediction methods, and model calibration methods. We will introduce these topics in the context of building trustworthy machine learning solutions to safety-critical applications and socially-responsible applications. For example, a typical application is responsible decision-making under uncertainty in non-stationary environments. So we will also introduce concepts like fair machine learning and learning under safety constraints, and discuss how robust and uncertainty-aware learning techniques contribute to such more desired systems. Students will learn the state-of-the-art methods in lectures, test their understanding in homeworks, and apply these methods in a project. Prerequisite(s): Students who have taken, or are currently enrolled in EN.601.687 OR EN.601.787 , are not eligible to take EN.601.487 .; EN.601.475 OR EN.601.675 Distribution Area: Engineering EN.601.489. Human-in-the-Loop Machine Learning. 4 Credits. Machine learning (ML) is being deployed in increasingly consequential tasks, such as healthcare and autonomous driving. For the foreseeable future, successfully deploying ML in such settings will require close collaboration and integration with humans, whether they be users, designers, engineers, policy-makers, etc. This course will look at how humans can be incorporated into the foundations of ML in a principled way. The course will be broken down into three parts: demonstration, collaboration, and oversight. Demonstration is about how machines can learn from 'observing' humans---such as learning to drive a car from data collected while humans drive. In this setting, the human is assumed to be strictly better than the machine and so the primary goal is to transmit the human's knowledge and abilities into the ML model. The second part, collaboration, is about when humans and models are near equals in performance but not in abilities. A relevant setting is AI-assisted healthcare: perhaps a human radiologist and ML model are good at diagnosing different kinds of diseases. Thus we will look at methodologies that allow machines to ‘ask for help' when they are either unconfident in their own performance and/or think the human can better carry out the task. The course will close with the setting in which machines are strictly better at a task than humans are, but we still wish to monitor them to ensure safety and alignment with our goals (oversight). Assessment will be done with homework, quizzes, and a final project. Prerequisite(s): Students who have taken or are enrolled in EN.601.689 are not eligible to take EN.601.489 .; EN.601.475 OR EN.601.675 Distribution Area: Engineering EN.601.490. Introduction to Human-Computer Interaction. 3 Credits. This course is designed to introduce undergraduate and graduate students to design techniques and practices in human-computer interaction (HCI), the study of interactions between humans and computing systems. Students will learn design techniques and evaluation methods, as well as current practices and exploratory approaches, in HCI through lectures, readings, and assignments. Students will practice various design techniques and evaluation methods through hands-on projects focusing on different computing technologies and application domains. This course is intended for undergraduate and graduate students in Computer Science/Cognitive Science/Psychology. Interested students from different disciplines should contact the instructor before enrolling in this course. Recommended Background: Basic programming skills. Prerequisite(s): Students can receive credit for either EN.601.490 or EN.601.690 , but not both. Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.491. Human-Robot Interaction. 3 Credits. This course is designed to introduce advanced students to research methods and topics in human-robot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledge of various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. Prerequisite(s): Students can receive credit for EN.601.491 or EN.601.691 , but not both; EN.601.220 /EN.600.120 AND EN.601.226 /EN.600.226 Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.493. Accessible Computing. 3 Credits. This course is designed to introduce students to the principles, challenges, and opportunities in designing computing systems that are accessible to people with diverse abilities. Students will learn about assistive technologies, inclusive design methodologies, and the incorporation of accessibility in computer applications through lectures, readings, and projects. Prerequisite(s): Students may only receive credit for EN.601.493 OR EN.601.693 .; EN.601.290 OR EN.601.490 OR EN.601.690 Distribution Area: Engineering EN.601.496. Computer Integrated Surgery II - Teams. 3 Credits. This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project in teams of at least 3 students, under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students who prefer to do individual projects must register for EN.601.456 instead. Prerequisite(s): Students may receive credit for only one of EN.601.456 , EN.601.496 , OR EN.601.656 ; EN.601.455 or permission Distribution Area: Engineering AS Foundational Abilities: Science and Data (FA2) EN.601.501. Computer Science Workshop. 1 - 3 Credits. An applications-oriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.601.503. Independent Study. 1 - 3 Credits. Individual guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.601.507. Undergraduate Research. 1 - 3 Credits. Individual research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.601.509. Computer Science Internship. 1 Credit. Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study and credit assigned must be worked out in advance between the student and the faculty member involved. As a rule of thumb, 40 hours of work is equivalent to one credit. Permission required. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.601.513. Group Undergraduate Project. 1 - 3 Credits. Independent learning and application for undergraduates under the direction of a faculty member in the department. This course has a regular project group meeting that students are expected to attend. The individual project contributions, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.601.517. Group Undergraduate Research. 1 - 3 Credits. Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.601.519. Senior Honors Thesis. 3 Credits. The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publicly before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors. Computer science majors only. Students should have a 3.5 GPA in computer science courses at the end of their junior year and permission of faculty sponsor. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.601.520. Senior Honors Thesis. 1 - 3 Credits. For computer science majors only, a continuation of EN.601.519 . Recommended Course Background: EN.601.519 Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms. EN.601.556. Senior Thesis In CIS. 3 Credits. The student will undertake a substantial independent research project in the area of computer-integrated surgery, under joint supervision of a WSE faculty adviser and a clinician or clinical researcher at the Johns Hopkins Medical School. Prerequisite(s): You must request Customized Academic Learning using the Customized Academic Learning form found in Student Self-Service: Registration > Online Forms.;EN.600.445 or permission of instructor. EN.601.604. Brain & Computation. 1 Credit. Computational and network aspects of the brain are explored. The topics covered include structure, operation and connectivity of neurons, general network structure of the neural system, and the connectivity constraints imposed by pre- and post-natal neural development and the desirability of network consistency within a species. Both discrete and continuous aspects of neural computation are covered. Precise mathematical tools and analyses such as logic design, transient and steady state behavior of linear systems, and time and connectivity randomization are discussed. The concepts are illustrated with several applications. Memory formation from the synaptic level to the high level constructs are explored. Students are not expected to master any of the mathematical techniques but are expected to develop a strong qualitative appreciation of their power. Cerebellum, which has a simple network connectivity, will be covered as a typical system. Recommended course background: linear algebra, differential equations, probability, and algorithms. Prerequisite(s): Students can receive credit for EN.601.404 or EN.601.604 , but not both Distribution Area: Engineering EN.601.613. Software Defined Networks. 3 Credits. Software-Defined Networks (SDN) enable programmability of data networks and hence rapid introduction of new services. They use software-based controllers to communicate with underlying hardware infrastructure and direct traffic on a network. This model differs from that of traditional networks, which use dedicated hardware devices (i.e., routers and switches) to control network traffic. This technology is becoming a key part of web scale networks (at companies like Google and Amazon) and 5G/6G networks. Its importance will keep on growing. Many of today’s services and applications, especially when they involve the cloud, could not function without SDN. SDN allows data to move easily between distributed locations, which is critical for cloud applications. A major focus will be on how this technology will be used in 5G and 6G Networks. The course will cover basics of SDN, ongoing research in this area, and the industrial deployments. Required Course Background: computer networks. Prerequisite(s): Students can receive credit for EN.601.413 or EN.601.613 , but not both. Distribution Area: Engineering EN.601.614. Computer Networks. 3 Credits. Topics covered will include applications layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, Web caching and CDNS, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-pn programming assignments, homework and two exams. Required course background: C/C++ programming and data structures, or permission. Prerequisite(s): Students can only receive credit for EN.601.414 or EN.601.614 , but not both. Distribution Area: Engineering EN.601.615. Databases. 3 Credits. Same material as 601.415, for graduate students. Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. (www.cs.jhu.g.sjuku.top/~yarowsky/cs415.html) Required course background: Data Structures Prerequisite(s): Students may receive credit for only one of EN.600.315, EN.600.415, EN.601.315 , EN.601.415 , EN.601.615 . Distribution Area: Engineering EN.601.618. Operating Systems. 3 Credits. Same material as 601.418, for graduate students. This course covers fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. Required course background: Data Structures & Computer System Fundamentals Prerequisite(s): Students may receive credit for only one of EN.600.318, EN.600.418, EN.601.318 , EN.601.418 , EN.601.618 . Distribution Area: Engineering EN.601.619. Cloud Computing. 3 Credits. Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and low-latency requirements of real-time applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming. Prerequisite(s): Students may earn credit for EN.601.419 or EN.601.619 , but not both. Distribution Area: Engineering EN.601.620. Parallel Computing for Data Science. 3 Credits. This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. The course will not cover GPU deep-learning frameworks nor CUDA. The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience. Required course background: Data Structures, Computer System Fundamentals, and familiarity with Python. Prerequisite(s): Students may receive credit for only one of EN.601.320, EN.601.420 , OR EN.601.620 . Distribution Area: Engineering EN.601.621. Object Oriented Software Engineering. 3 Credits. Same material as EN.601.421 , for graduate students. This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. Required course background: intermediate programming, data structures, and experience in mobile or web app development. Students may receive credit for only one of 601.421/621. Prerequisite(s): Students may receive credit for only one of EN.600.321, EN.600.421, EN.601.421 , EN.601.621 . Distribution Area: Engineering EN.601.622. Software Testing & Debugging. 3 Credits. Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. Required course background: significant mobile or web app development. Prerequisite(s): Students can only take EN.601.422 or EN.601.622 , but not both. Distribution Area: Engineering EN.601.624. Reliable Software Systems. 3 Credits. Reliability is an essential quality requirement for all artifacts operating in the real-world, ranging from bridges, cars to power grids. Software systems are no exception. In this computing age when software is transforming even traditional mission-critical artifacts, making sure the software we write is reliable becomes ever more important. This course exposes students to the principles and techniques in building reliable systems. We will study a set of systematic approaches to make software more robust. These include but are not limited to static analysis, testing framework, model checking, symbolic execution, fuzzing, and formal verification. In addition, we will cover the latest research in system reliability. Recommended course background: EN.601.220 AND EN.601.628 . Prerequisite(s): Students may receive credit for EN.601.424 OR EN.601.624 , but not both. EN.601.625. Software System Design. 3 Credits. This course introduces modern software systems design, with an emphasis on how to design large-scale systems, assess common system design trade-offs, and tackle system design challenges. It covers non-functional requirements, API design, distributed systems concepts, modern software building blocks (e.g., load balancers, caches, containers, etc.). Additionally, it includes case studies of common system design problems, some drawn from interview questions. Ultimately, this course helps learners become better software engineers. Required course background: EN.601.315 /415/615 or EN.601.280 or EN.601.290 or EN.601.340 /440/640 or EN.601.421 /621), or permission. Students may receive credit for only one of 601.425/625. Prerequisite(s): Students may take EN.601.425 OR EN.601.625 for credit, but not both. EN.601.626. Principles of Programming Languages. 3 Credits. Same material as EN.601.426 , for graduate students. Functional, object-oriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. Required course background: EN.601.226 . Prerequisite(s): Students may only receive credit for one of the following: EN.601.426 or EN.601.626 . Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.628. Compilers & Interpreters. 3 Credits. Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, runtime environments, and code generation and optimization. Students are required to write a compiler as a course project. Required course background: intermediate programming, data structures and computer system fundamentalsRecommended background: automata and computation theory Prerequisite(s): Students may receive credit for only one of EN.601.428 or 601.628. Distribution Area: Engineering EN.601.629. Functional Programming in Software Engineering. 3 Credits. How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project. Required course background in data structures ( EN.601.226 ) Prerequisite(s): Students can receive credit for EN.601.429 or EN.601.629 , but not both. Distribution Area: Engineering EN.601.630. Combinatorics & Graph Theory in Computer Science. 3 Credits. This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. Required Course Background: discrete math, probability theory and linear algebra. Prerequisite(s): Students may receive credit for EN.601.430 or EN.601.630 , but not both. Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.631. Theory of Computation. 3 Credits. This course covers the theoretical foundations of computer science. Topics included will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.431/601.631, unless one is for an undergrad degree and the other for grad. Required Background: discrete math or permission; discrete probability theory recommended. Prerequisite(s): Students can receive credit for only one of EN.601.431 / EN.601.631 Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.633. Intro Algorithms. 3 Credits. Same material as EN.601.433 , for graduate students.This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. Required Background: data structures, discrete math, proof writing. Prerequisite(s): Students may receive credit for only one of EN.600.363, EN.600.463, EN.601.433 , EN.601.633 Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.634. Randomized and Big Data Algorithms. 3 Credits. This course will cover fundamental methods of randomization for efficient algorithms and relevant techniques in probabilistic analysis. The first part of the course will discuss classical randomized algorithms, including the randomized algorithm for the min-cut problem, hashing techniques, tail inequalities, and the probabilistic method. The second part will delve into advanced topics such as coreset methods for clustering algorithms, the Johnson-Lindenstrauss lemma, and the applications of streaming and sketching algorithms. Prerequisite(s): Students may receive credit for only one of EN.600.464, EN.600.664, EN.601.434 , EN.601.634 . Distribution Area: Engineering EN.601.635. Approximation Algorithms. 3 Credits. Graduate version of EN.601.435 . This course provides an introduction to approximation algorithms. Topics include vertex cover, TSP, Steiner trees, cuts, greedy approach, linear and semi-definite programming, primal-dual method, and randomization. Additional topics will be covered as time permits. There will be a final project. Recommended Background: EN.601.633 or equivalent. Students may receive credit for EN.601.435 or EN.601.635 , but not both. Prerequisite(s): Students can only receive credit for one of EN.601.435 or EN.601.635 . EN.601.636. Algorithmic Game Theory. 3 Credits. Same material as EN.601.436 , for graduate students. This course provides an introduction to algorithmic game theory: the study of games from the perspective of algorithms and theoretical computer science. There will be a particular focus on games that arise naturally from economic interactions involving computer systems (such as economic interactions between large-scale networks, online advertising markets, etc.), but there will also be broad coverage of games and mechanisms of all sorts. Topics covered will include a) complexity of computing equilibria and algorithms for doing so, b) (in)efficiency of equilibria, and c) algorithmic mechanism design. Prerequisite(s): Students may receive credit for EN.601.436 or EN.601.636 , but not both. Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.638. Algorithmic Foundations of Differential Privacy. 3 Credits. This course provides an introduction to differential privacy, with a focus on algorithmic aspects (rather than statistical or engineering aspects). Specific topics we will cover include: motivation for differential privacy, and different versions of differential privacy (pure, approximate, Renyi, and zero-concentrated in particular); basic mechanisms (Laplace, Gaussian, Discrete Gaussian, and Exponential); composition theorems; basic algorithmic techniques (sparse vector technique, private multiplicative weights, private selection); beyond global sensitivity: local sensitivity, propose-test-release, subsampling; differentially private graph algorithms; lower bounds. Required Course Background: 601.433/633 or permission. Students may receive credit for only one of 601.438/638. Prerequisite(s): Students may earn credit for EN.601.438 or EN.601.638 , but not both. Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.640. Web Security. 3 Credits. This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. Required course background: data structures, computer system fundamentals and javascript/web development. Students may receive credit for only one of 601.340/440/640. Prerequisite(s): Students may receive credit for only one of 601.340/440/640. EN.601.641. Blockchains and Cryptocurrencies. 3 Credits. Same as EN.601.441 , for graduate students. This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. Recommended Course Background: EN.601.226 AND ( EN.553.310 OR EN.553.420 ) Prerequisite(s): Students may receive credit for only one of EN.600.451 OR EN.601.441 OR EN.601.641 Distribution Area: Engineering EN.601.642. Modern Cryptography. 3 Credits. Same material as 601.442, for graduate students. Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zero-knowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. Required course background: Probability & Automata/Computation Theory Prerequisite(s): Students may receive credit for only one of EN.601.442 OR EN.601.642 . Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.643. Security & Privacy in Computing. 3 Credits. Same material as 601.443, for graduate students. Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. Required course background: C programming and computer system fundamentals. Prerequisite(s): Students may receive credit for only one of EN.600.443, EN.601.443 , EN.601.643 . Distribution Area: Engineering EN.601.644. Medical Device Cybersecurity. 3 Credits. In an increasingly connected healthcare landscape, medical devices have effectively become IT endpoints, often running general-purpose operating systems like Windows or Linux, incorporating cloud microservices, and integrating artificial intelligence to detect, prevent, and improve patient health outcomes. Protecting these devices from cyber threats is not just a technical challenge—it's a matter of patient safety. A security breach in medical devices like pacemakers or infusion pumps can have life-threatening consequences. National and international regulatory bodies, such as the FDA and EU National Competent Authorities (NCAs) and Medical Device Regulation (MDR), know the implications and have provided prescription and guidance emphasizing stringent cybersecurity measures throughout a device's lifecycle, from design and development to postmarket surveillance. The result is a heightened awareness of medical device security and its impact on healthcare delivery, requiring cybersecurity risk management. In particular, focusing on threat modeling, cybersecurity risk assessment, secure design, secure coding practices, vulnerability management and monitoring, software bill of materials, cybersecurity transparency, user labeling, penetration testing, and more. Recommended background: computing systems, operating systems, machine learning & AI. Students may receive credit for only one of 601.444/601.644. Prerequisite(s): Students who have already taken, or are currently enrolled in EN.601.444 , are not eligible to take EN.601.644 .; EN.601.443 OR EN.601.643 EN.601.645. Practical Cryptographic Systems. 3 Credits. Same material as 601.445, for graduate students. This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem.We will place a particular emphasis on the techniques of provable security and the feasibility of reverse-engineering undocumented cryptographic systems. Prerequisite(s): Students may receive credit for EN.600.454/ EN.601.445 or EN.601.645 , but not both. Distribution Area: Engineering EN.601.646. Sketching and Indexing for Sequences. 3 Credits. Many of the world's largest and fastest-growing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing --- succinct and compressed indexes in particular -- which enables us to efficiently search inside very long strings, especially in highly repetitive texts. Prerequisite(s): Students may receive credit for EN.601.446 or EN.601.646 , but not both. EN.601.647. Computational Genomics: Sequences. 3 Credits. Same material as 601.447, for graduate students. Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming projects. Required course background: Intermediate programming (C/C++) and Data Structures Prerequisite(s): Students may receive credit for only one EN.601.447 /647/747 Distribution Area: Engineering EN.601.649. Computational Genomics: Applied Comparative Genomics. 3 Credits. The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. Prerequisites: knowledge of the Unix operating system and programming expertise in a language such as R or Python. Prerequisite(s): Students may receive credit for only one of EN.601.449 / EN.601.649 /EN.601.749. EN.601.651. Introduction to Computational Immunogenomics. 3 Credits. Immunology studies defensive mechanisms of living organisms against external threats. Computational immunogenomics is a new field of bioinformatics that develops and applies computational approaches to the study and interpretation of immunological data, seeking to answer questions about adaptive immune responses in humans and important animals. In this course, students will learn how to design, apply, and benchmark algorithms for solving immunogenomics problems. Required Course Background: Intermediate Programming & Data Structures. Students may receive credit for only one of EN.601.451 , EN.601.651 . Prerequisite(s): Students may receive credit for only one of EN.601.451 OR EN.601.651 . EN.601.653. Applications of Augmented Reality. 3 Credits. This course is designed to expand the student’s augmented reality knowledge and introduce relevant topics necessary for developing more meaningful applications and conducting research in this field. The course addresses the fundamental concepts of visual perception and introduces non-visual augmented reality modalities, including auditory, tactile, gustatory, and olfactory applications. The following sessions discuss the importance of integrating user-centered design concepts to design meaningful augmented reality applications. A later module introduces the basic requirements to design and conduct user studies and guidelines on interpreting and evaluating the results from the studies. During the course, students conceptualize, design, implement and evaluate the performance of augmented reality solutions for their use in industrial applications, teaching and training, or healthcare settings. Homework in this course will relate to applying the theoretical methods used for designing, implementing, and evaluating augmented reality applications. Required course background: intermediate programming (C/C++), data structures, linear algebra; EN.601.654 preferred. Prerequisite(s): Students may receive credit for only one of EN.601.453 or EN.601.653 , but not both.; EN.601.454 OR EN.601.654 Distribution Area: Engineering EN.601.654. Introduction to Augmented Reality. 3 Credits. This course introduces students to the field of Augmented Reality. It reviews its basic definitions, principles, and applications. The course explains how fundamentals concepts of computer vision are applied for the development of Augmented Reality applications. It then focuses on describing the principal components and particular requirements to implement a solution using this technology. The course also discusses the main issues of calibration, tracking, multi-modal registration, advanced visualization, and display technologies. Homework in this course will relate to the mathematical methods used for calibration, tracking, and visualization in augmented reality. Required course background: intermediate programming (C/C++), data structures, linear algebra. Prerequisite(s): Students may receive credit for only on EN.601.454 / EN.601.654 Distribution Area: Engineering, Natural Sciences EN.601.655. Computer Integrated Surgery I. 4 Credits. Same material as 601.455, for graduate students. This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. Required Course Background: data structures and linear algebra or permission.Recommended Course Background: intermediate programming in C/C++, EN.601.457 , EN.601.461 , image processing. Prerequisite(s): Students may receive credit for only one of EN.601.455 or EN.601.655 . Distribution Area: Engineering EN.601.656. Computer Integrated Surgery II. 3 Credits. Same material as EN.601.456 , for graduate students. This weekly lecture/seminar course addresses similar material to EN.601.655 , but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from EN.601.655 , although it does not have to be. Grades are based both on the project and on classroom recitations. Students wishing to attend the weekly lectures as a 1-credit seminar should sign up for EN.601.356 . Students may receive credit for only one of 601.456/496/656. Prerequisite(s): Students may receive credit for only one of EN.600.446, EN.600.646, EN.601.456 , EN.601.496 , OR EN.601.656 .;EN.600.445/ EN.601.455 OR EN.600.645/ EN.601.655 OR permission of the instructor. EN.601.657. Computer Graphics. 3 Credits. Same material as 601.457, for graduate students. This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. Permission of instructor is required for students not satisfying a pre-requisite. No Audits.Required course background: EN.601.220 (C++), EN.601.226 , linear algebra. Prerequisite(s): Students may receive credit for only one of EN.601.457 OR EN.601.657 . Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.661. Computer Vision. 3 Credits. This course provides an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3¬D geometry from binocular stereo, motion, and photometric stereo, and object recognition, image segmentation, and activity analysis. Elements of machine learning and deep learning are also included. Required course background: Intro to Programming, Linear Algebra & prob/stats Prerequisite(s): Students may receive credit for only one of EN.601.461 , EN.601.661 , OR EN.601.761. Distribution Area: Engineering EN.601.663. Algorithms for Sensor-Based Robotics. 3 Credits. Same material as EN.601.463 , for graduate students. This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. Recommended Course Background: EN.601.226 , linear algebra, calculus, probability. Prerequisite(s): Students may receive credit for only one of 601.463/663/763 Distribution Area: Engineering EN.601.664. Artificial Intelligence. 3 Credits. The course situates the study of Artificial Intelligence (AI) first in the broader context of Cognitive Science (i.e., human intelligence) and then treats in-depth principles and methods for reasoning, planning, and learning, including both conventional methods and recent deep learning approaches. The class is recommended for all scientists and engineers with a genuine curiosity about how to build an AI system (in particular, an intelligent agent) that can learn, reason about, and interact with the world and other agents. Students will be asked to complete both programming assignments and writing assignments. Required course background: data structures, linear algebra & prob/stat. Students can only receive credit for one of 601.464/664. Prerequisite(s): Students may receive credit for only one of EN.601.464 OR EN.601.664 . Distribution Area: Engineering EN.601.665. Natural Language Processing. 4 Credits. Same material as 601.465, for graduate students. An in-depth introduction to core techniques for analyzing, transforming, and generating human language. The course spans linguistics, modeling, algorithms, and applications. (1) How should linguistic structure and meaning be represented (e.g., trees, morphemes, ?-terms, vectors)? (2) How can we formally model the legal structures and their probabilities (e.g., grammars, automata, features, log-linear models, recurrent neural nets, Transformers)? (3) What algorithms can estimate the parameters of these models (e.g., gradient descent, EM) and efficiently identify probable structures (e.g., dynamic programming, beam search)? (4) Finally, what kinds of systems can be built with these techniques and how are they constructed and evaluated in practice? Detailed assignments guide students through many details of implementing core NLP methods. The course proceeds from first principles, although prior exposure to AI, statistics, ML, or linguistics can be helpful. Prerequisite: Data Structures and basic familiarity with Python, partial derivatives, matrix multiplication and probabilities. Prerequisite(s): Students may receive credit for only one of EN.601.465 OR EN.601.665 . Distribution Area: Engineering EN.601.666. Information Retrieval and Web Agents. 3 Credits. Same material as EN.601.466 , for graduate students. An in-depth, hands-on study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. [Applications] Recommended Course Background: EN.601.226 Prerequisite(s): Students can receive credit for EN.601.466 or EN.601.666 , but not both Distribution Area: Engineering EN.601.667. Introduction to Human Language Technology. 3 Credits. This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; informationextraction; knowledge representation; machine translation; dialog systems; etc. Required Background: EN.601.226 Data Structures; knowledge of Python recommended. Prerequisite(s): Students can receive credit for only one of 601.467/667. EN.601.668. Machine Translation. 3 Credits. Same material as 601.468, for graduate students. Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. Required course background: Data Structures and prob/stats Prerequisite(s): Students may receive credit for only one of EN.601.468 OR EN.601.668 . Distribution Area: Engineering EN.601.669. AI Safety, Alignment, & Governance. 3 Credits. This course will focus on the alignment and governance challenges posed by advanced frontier/general purpose AI models: why these models may behave in ways that pose significant risk to human welfare and what technical and governance approaches might mitigate these risks. We’ll begin the course studying general results from alignment and governance in human normative systems such as markets, politics, norms and laws. We’ll pay special attention to risks arising from agentic AI. We’ll then look at current technical and position papers in various topics in AI safety and alignment. Topics could include: RLHF, constitutional AI, red-teaming, safety evaluation methods, red lines, jail-breaking, prompt injection, over-optimization, and open-source debates. We’ll conclude with discussion of regulatory frameworks such as regulatory markets, registration of frontier models, international governance organizations, registration of AI agents and legal personhood for AI agents. This is a paper-reading class. Prerequisite(s): Students may only receive credit for EN.601.469 OR EN.601.669 . EN.601.670. Artificial Agents. 3 Credits. This course covers a number of topics explored in introductory AI, such as knowledge representation, reasoning, and natural language understanding. Unlike introductory AI, we will pursue these topics based on the transformer neural architecture. We will motivate the material through interacting with agents in games: how to build models that understand user commands, how to generate responses back to a user, and how to reason about a synthetic environment to determine a course of action. Assignments will include programming, presentations on readings, and written summaries of readings. EN.601.671. Natural Language Processing: Self-Supervised Models. 3 Credits. The rise of massive self-supervised (pre-trained) models have transformed various data-driven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to self-supervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own self-supervised neural network models, using the Pytorch framework. Required course background: data structures, linear algebra, probability, familiarity with Python/PyTorch, natural language processing or machine learning. Prerequisite(s): Students may receive credit for EN.601.471 or EN.601.671 , but not both. Distribution Area: Engineering EN.601.672. Natural Language Processing for Computational Social Science. 3 Credits. [Alt. title: Analyzing Text as Data] Vastly available digitized text data has created new opportunities for understanding social phenomena. Relatedly, social issues like toxicity, discrimination, and propaganda frequently manifest in text, making text analyses critical for understanding and mitigating them. In this course, we will centrally explore: how can we use NLP as a tool for understanding society? Students will learn core and recent advances in text-analysis methodology, building from word-level metrics to embeddings and language models as well as incorporating statistical methods such as time series analyses and causal inference. Required Course Background: natural language processing and familiarity with Python/PyTorch. Students may receive credit for EN.601.472 or EN.601.672 , but not both. Prerequisite(s): Students can only receive credit for one of the following: EN.601.472 OR EN.601.672 . EN.601.673. Cognitive Artificial Intelligence. 3 Credits. Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment. Required Course Background: Prob/Stat & Linear Algebra & Computing; prior course in ML/AI strongly recommended.Students may receive credit for only one of 601.473/601.673. Prerequisite(s): Students who have taken EN.601.473 are not eligible to take EN.601.673 . EN.601.674. ML: Learning Theory. 3 Credits. This machine learning course will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. EN.601.675. Machine Learning. 3 Credits. Same material as 601.475, for graduate students. Machine learning is subfield of computer science and artificial intelligence, whose goal is to develop computational systems, methods, and algorithms that can learn from data to improve their performance. This course introduces the foundational concepts of modern Machine Learning, including core principles, popular algorithms and modeling platforms. This will include both supervised learning, which includes popular algorithms like SVMs, logistic regression, boosting and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include a heavy programming components, requiring students to implement several machine learning algorithms in a common learning framework. Additionally, analytical homework questions will explore various machine learning concepts, building on the pre-requisites that include probability, linear algebra, multi-variate calculus and basic optimization. Students in the course will develop a learning system for a final project. Required course background: multivariable calculus, probability, linear algebra, intro to computing Prerequisite(s): Students may receive credit for only one of EN.601.475 OR EN.601.675 . Distribution Area: Engineering EN.601.677. Causal Inference. 3 Credits. "Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Prerequisite(s): Students may receive credit for only one of EN.601.477 OR EN.601.677 . Distribution Area: Engineering, Quantitative and Mathematical Sciences EN.601.679. Machine Learning: Reinforcement Learning. 3 Credits. Tremendous success of reinforcement learning (RL) in a variety of settings from AlphaGo to LLMs makes it a critical area to study. This course will study classical aspects of RL as well as its modern counterparts. Topics will include Markov Decision Processes, dynamic programming, model-based and model-free RL, temporal difference learning, Monte Carlo methods, multi-armed bandits, policy optimization and other methods.Required course background: machine learning, linear algebra and probability. Students may receive credit for at most one of 601.479/679. Prerequisite(s): Students who have taken, or are currently enrolled in EN.601.479 , are not eligible to take EN.601.679 . Distribution Area: Engineering EN.601.682. Machine Learning: Deep Learning. 4 Credits. Deep learning (DL) has emerged as a powerful tool for solving data-intensive learning problems such as supervised learning for classification or regression, dimensionality reduction, and control. As such, it has a broad range of applications including speech and text understanding, computer vision, medical imaging, and perception-based robotics. The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics. Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. Required course background: Data Structures, Linear Algebra, Probability, Calc II required; Statistics, Machine Learning, Calc III, numerical optimization and Python strongly recommended. Prerequisite(s): Students may receive credit for EN.601.482 or EN.601.682 , but not both. Distribution Area: Engineering EN.601.684. Explainable AI Design & Human-AI Interaction. 3 Credits. This is a design course. Increasing the trustworthiness of machine learning solutions has emerged as an important research area. One approach to trustworthy machine learning is explainable and/or interpretable machine learning, which attempts to reveal the working mechanisms of a machine learning system. However, other than on-task performance, explainability is not a property of machine learning algorithms, but an affordance: a relationship between explanation model and the target users in their context. Successful development of machine learning solutions that afford explainability thus requires understanding of techniques beyond pure machine learning. In this course, we will first review the basics of machine learning and human-centered design. Then, we will introduce several techniques to explain machine learning models and/or make them interpretable, and through hands-on sessions and case studies, will investigate how these techniques affect human-AI interaction.In addition to individual homework assignments, students will work in groups to design, justify, implement, and test an explainable machine learning algorithm for a problem of their choosing.Additional Recommended (601.454/654, 601.290, 601.490/690 or 601.491/691) and 601.477/677. Prerequisite(s): Students may receive credit for EN.601.484 or EN.601.684 , but not both. EN.601.685. Probabilistic Models of the Visual Cortex. 3 Credits. The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. Programming experience (Python preferred). Prerequisite(s): Students who have taken AS.050.375 / AS.050.675 are not eligible to take EN.601.685 . Distribution Area: Quantitative and Mathematical Sciences EN.601.686. Machine Learning: Artificial Intelligence System Design & Development. 3 Credits. Advances in Artificial intelligence have opened new opportunities for developing systems to aid in numerous areas of society. In order for AI systems to succeed in making constructive and positive changes, we must consider their impact on everyday life. Specifically, AI system designers must evaluate the overall capabilities of the system, consider the resulting human-AI interactions, and ensure that the system behaves in a responsible and ethical manner. In this project-based course you will work in teams of 3-5 students to 1) Identify a need with high-impact implications on everyday life; 2) Articulate principles of Responsible AI relevant to the intended application, 3) Conceptualize and design an AI system targeting this need, and 4) Develop the AI system by refining a demo-able prototype based on feedback received during course presentations. Additionally, we will discuss potential ethical issues that can arise in AI and how to develop Responsible AI principles. Coursework will consist of writing assignments, project presentations, and a project demonstration.Required course background: ( EN.601.475 /675 or EN.601.464 /664 or EN.601.482 /682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design). Prerequisite(s): ( EN.601.475 OR EN.601.675 ) OR ( EN.601.464 OR EN.601.664 ) OR ( EN.601.482 OR EN.601.682 ) Distribution Area: Engineering EN.601.687. Machine Learning: Coping with Non-Stationary Environments and Errors. 3 Credits. This course teaches machine learning methods that 1) consider data distribution shift and 2) represent and quantify the model uncertainty in a principled way. The topics we will cover include machine learning techniques that deal with data distribution shift, including domain adaptation, domain generalization, and distributionally robust learning techniques, and various uncertainty quantification methods, including Bayesian methods, conformal prediction methods, and model calibration methods. We will introduce these topics in the context of building trustworthy machine learning solutions to safety-critical applications and socially-responsible applications. For example, a typical application is responsible decision-making under uncertainty in non-stationary environments. So we will also introduce concepts like fair machine learning and learning under safety constraints, and discuss how robust and uncertainty-aware learning techniques contribute to such more desired systems. Students will learn the state-of-the-art methods in lectures, test their understanding in homeworks, and apply these methods in a project.Required course background: 601.475/675 Machine Learning. Students may receive credit for only one of 601.487/687, and may not take this course after taking EN.601.787 . Prerequisite(s): Students who have taken or are enrolled in EN.601.787 OR EN.601.487 are not eligible to take EN.601.687 . EN.601.689. Human-in-the-Loop Machine Learning. 3 Credits. Machine learning (ML) is being deployed in increasingly consequential tasks, such as healthcare and autonomous driving. For the foreseeable future, successfully deploying ML in such settings will require close collaboration and integration with humans, whether they be users, designers, engineers, policy-makers, etc. This course will look at how humans can be incorporated into the foundations of ML in a principled way. The course will be broken down into three parts: demonstration, collaboration, and oversight. Demonstration is about how machines can learn from 'observing' humans---such as learning to drive a car from data collected while humans drive. In this setting, the human is assumed to be strictly better than the machine and so the primary goal is to transmit the human's knowledge and abilities into the ML model. The second part, collaboration, is about when humans and models are near equals in performance but not in abilities. A relevant setting is AI-assisted healthcare: perhaps a human radiologist and ML model are good at diagnosing different kinds of diseases. Thus we will look at methodologies that allow machines to ‘ask for help' when they are either unconfident in their own performance and/or think the human can better carry out the task. The course will close with the setting in which machines are strictly better at a task than humans are, but we still wish to monitor them to ensure safety and alignment with our goals (oversight). Assessment will be done with homework, quizzes, and a final project.Required course background: EN.601.475 /675 Machine Learning or equivalent. Prerequisite(s): Students who have taken or are enrolled in EN.601.489 are not eligible to take EN.601.689 . EN.601.690. Introduction to Human-Computer Interaction. 3 Credits. This course is designed to introduce undergraduate and graduate students to design techniques and practices in human-computer interaction (HCI), the study of interactions between humans and computing systems. Students will learn design techniques and evaluation methods, as well as current practices and exploratory approaches, in HCI through lectures, readings, and assignments. Students will practice various design techniques and evaluation methods through hands-on projects focusing on different computing technologies and application domains. This course is intended for undergraduate and graduate students in Computer Science/Cognitive Science/Psychology. Interested students from different disciplines should contact the instructor before enrolling in this course. Recommended Background: Basic programming skills. Prerequisite(s): Students can receive credit for either EN.601.490 or EN.601.690 , but not both. Distribution Area: Engineering EN.601.691. Human-Robot Interaction. 3 Credits. This course is designed to introduce graduate students to research methods and topics in human-robot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledgeof various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. Pre-req: EN.601.220 and EN.601.226 . Prerequisite(s): Students may receive credit for EN.601.491 or EN.601.691 . EN.601.693. Accessible Computing. 3 Credits. This course is designed to introduce students to the principles, challenges, and opportunities in designing computing systems that are accessible to people with diverse abilities. Students will learn about assistive technologies, inclusive design methodologies, and the incorporation of accessibility in computer applications through lectures, readings, and projects.Required Background: programming and knowledge in human-computer interaction. Prerequisite(s): Students may only receive credit for EN.601.493 OR EN.601.693 . EN.601.713. Future Networks. 3 Credits. Early networks were used for short message exchanges (Telegraph), and then the world moved to voice telephony. Today, the Internet’s dominant traffic is entertainment video. More and more objects (IoT devices) are connected to the Internet for control and monitoring. With the need for enormous AI computations, new networks with gigantic capacity are being designed and built. These transformations require transferring large amounts of information, rapidly deploying new features, and simpler management.The course will start with a brief introduction to the past networks: telegraph and telephone networks. Then, it will move to today's Internet. Endpoints are not just humans but also objects and machines; the Internet is increasingly becoming a network of objects.The course will mostly focus on how these networks will evolve in the future. New applications such as autonomous driving require networking and computing to be embedded together. This feature is already beginning to be implemented in 5G and 6G networks; 6G will also allow networks to be used as sensors. New technologies such as mobile edge computing, software-defined networking (SDN), network slicing, digital twins, and named-data networking (NDN) enable these advances. Two timely topics – Web 3 and the application of machine learning to networking – have been added. V2X networks will be a strong focus.Students will be required to participate in discussions on this topic. Students will be asked to study new papers and do course projects, which should result in longer-term research projects.Recommended Course Background: A course in computer networks (e.g., EN.601.414 /614 Computer Network Fundamentals). EN.601.714. Advanced Computer Networks. 3 Credits. This is a graduate-level course on computer networks. It provides a comprehensive overview on advanced topics in network protocols and networked systems. The course will cover both classic papers on Internet protocols and recent research results. It will examine a wide range of topics, e.g., routing, congestion control, network architectures, datacenter networks, network virtualization, software-defined networking, and programmable networks, with an emphasize on core networking concepts and principles. The course will include lectures, paper discussions, programming assignments and a research project. Recommended Course Background: One undergraduate course in computer networks (e.g., EN.601.414 /614 Computer Network Fundamentals or the equivalent), or permission of the instructor. The course assignments and projects assume students to be comfortable with programming. EN.601.715. Advanced Networks: Internet Measurement. 3 Credits. This course will be an introduction to Internet measurement, and especially how it relates to security and policy. This course builds on the topics in EN 601.414/614, discussing vulnerabilities of internetworking protocols (BGP), the domain name system (DNS), and HTTPS certificate management. The goal of this course is to learn about current research, and get hands-on experience with real Internet measurement data. This data will help reveal the structure of the modern Internet, and the financial relationships that continue to shape it.Required Course Background: An undergraduate course in computer networks (e.g., EN.601.414 /614 Computer Network Fundamentals or the equivalent), or permission of the instructor. EN.601.716. Advanced Topics in Internet of Things. 3 Credits. This course explores the convergence of computer networks, mobile computing, and embedded systems, with a specific focus on the Internet of Things (IoT). IoT represents a paradigm shift in computing, aiming to bridge the gap between the physical and digital worlds. Its development has opened up new possibilities, including mobile health, smart homes, industrial automation, and more. Throughout the course, students will delve into advanced topics such as IoT networking, mobile and edge computing, embedded machine learning, wireless sensing, human-computer interaction, and mobile health applications. To excel in this course, students are expected to engage in pre-class readings and in-class discussions, and complete a semester-long project. The focus of the course will be on training research philosophy and principles instead of papers' technical details. The course covers multiple disciplines and encourages interdisciplinary projects; students with diverse backgrounds such as computer science/engineering, electrical engineering, biomedical engineering or other related areas are welcomed. [Systems] Recommended Course Background: familiarity with computer system fundamentals, computer networks, signal processing, and mobile computing. EN.601.717. Advanced Distributed Systems & Networks. 3 Credits. The course explores the state of the art in distributed systems, networks and Internet research and practice, trying to see what it would take to push the envelop a step further. The course is conducted as a discussion group, where the professor and students brainstorm and pick interesting semester-long projects with high potential future impact. Example areas include robust scalable infrastructure (distributed datacenters, cloud networking, scada systems), real-time performance (remote surgery, trading systems), hybrid networks (mesh networks, 3-4G/Wifi/Bluetooth). Students should feel free to bring their own topics of interest and ideas. Recommended Course Background: a systems course (distributed systems, operating systems, computer networks, parallel programming) or permission of instructor. EN.601.727. Machine Programming. 3 Credits. Programs are the fundamental medium through which humans interact with computers. With the advent of large language models (LLMs), the automated synthesis of programs is rapidly transforming how we build software. Instead of manual code writing, we specify intent through examples, specifications, and natural language.This course explores both the foundations and frontiers of program synthesis, covering traditional symbolic techniques alongside LLM-driven approaches. Students will study a variety of synthesis paradigms, including example-based, type- and specification-guided, and interactive methods. We will examine how LLMs are applied to general-purpose programming tasks as well as to specialized domains such as theorem proving, program repair, planning, and verification.Throughout the course, students will gain exposure to a wide range of programming languages, from widely-used ones like Python and C, to emerging and domain-specific languages such as Rust, Lean, CodeQL, and PDDL. The course offers a research-oriented perspective combined with hands-on assignments and projects, providing students with both conceptual understanding and practical experience at the intersection of programming languages and machine learning.Required course background: Python proficiency and LLM familiarity. EN.601.740. Language-based Security. 3 Credits. This course will introduce Language-based Security, an emerging field in cyber security that leverages techniques from compilers and program analysis for security-related problems. Topics include but are not limited to: Control-flow and data-flow graphs, Program slicing, Code property graph (CPG), and Control-flow integrity. Students are expected to read new and classic papers in this area and discuss them in class. Recommended backgrounds are Operating Systems and preferably Compilers. EN.601.742. Advanced Topics in Cryptography. 3 Credits. This course will focus on advanced cryptographic topics with an emphasis on open research problems and student presentations. Prerequisite(s): EN.601.442 / EN.601.642 OR EN.601.445 / EN.601.645 OR Permission of Instructor. EN.601.743. Advanced Topics in Computer Security. 3 Credits. Topics will vary from year to year, but will focus mainly on network perimeter protection, host-level protection, authentication technologies, intellectual property protection, formal analysis techniques, intrusion detection and similarly advanced subjects. Emphasis in this course is on understanding how security issues impact real systems, while maintaining an appreciation for grounding the work in fundamental science. Students will study and present various advanced research papers to the class. There will be homework assignments and a course project. A college level security or crypto course at Hopkins or any other school is required. EN.601.760. FFT in Graphics & Vision. 3 Credits. In this course, we will study the Fourier Transform from the perspective of representation theory. We will begin by considering the standard transform defined by the commutative group of rotations in 2D and translations in two- and three-dimensions, and will proceed to the Fourier Transform of the non-commutative group of 3D rotations. Subjects covered will include correlation of images, shape matching, computation of invariances, and symmetry detection. Recommended Course Background: AS.110.201 and comfort with mathematical derivations.No Audits. EN.601.763. Advanced Topics in Robot Perception. 3 Credits. The goal of this course is to explore machine learning and perception algorithms focused on robotic applications. Topics will include robot localization and mapping, pedestrian/obstacle detection/prediction, semantic segmentation, perception-based grasp planning, continual learning for perception algorithms and multimodal sensor fusion. This course will include introductions to the topics by the instructor followed by paper reading and discussions led by the students. In addition, this course will consist of an in-depth semester long project that will emphasize research skills including developing a hypothesis, conducting literature reviews, formulating the problem, defining, and conducting experiments and finally evaluating and reporting results. Required Course Background: Programming, Linear Algebra, Prob/Stat, Computer Vision and (Machine Learning or ML: Deep Learning). Prerequisite(s): Students may only earn credit for one of the following: EN.600.336, EN.600.436/ EN.601.463 , EN.600.663, or EN.600.636/ EN.601.763 . Distribution Area: Engineering, Natural Sciences EN.601.764. Advanced NLP: Multilingual Methods. 3 Credits. This is a project based course focusing on the design and implementation of systems that scale Natural Language Processing methods beyond English. The course will cover both multilingual and cross-lingual methods with an emphasis on zero-shot and few-shot approaches, as well as ‘silver’ dataset creation. Modules will include Cross-Lingual Information Extraction & Semantics, Cross-Language Information Retrieval, Multilingual Question Answering, Multilingual Structured Prediction, Multilingual Automatic Speech Recognition, as well as other non-English centric NLP methods. Students will be expected to work in small groups and pick from one of the modules to create a model based on state-of-the-art methods covered in the class. The course will be roughly two-thirds lecture based and one-third students presenting project updates periodically throughout the semester. Prerequisite(s): EN.601.465 OR EN.601.665 Distribution Area: Engineering, Natural Sciences EN.601.770. AI Ethics and Social Impact. 3 Credits. AI is poised to have an enormous impact on society. What should that impact be and who should get to decide it? The goal of this course is to critically examine AI research and deployment pipelines, with in-depth examinations of how we need to understand social structures to understand impact. In application domains, we will examine questions like “who are key stakeholders?”, “who is affected by this technology?” and “who benefits from this technology?”. We will also conversely examine: how can AI help us learn about these domains, and can we build from this knowledge to design AI for "social good"? As a graduate-level course, topics will focus on current research including development and deployment of technologies like large language models and decision support tools, and students will conduct a final research project. Required Course Background: At least one graduate-level computer science course in Artificial Intelligence or Machine Learning (including NLP, Computer Vision, etc.), two preferred, or permission of the instructor. EN.601.771. Advances in Self-Supervised Statistical Models. 3 Credits. The rise of massive self-supervised (pre-trained) models has transformed various data-driven fields such as natural language processing, computer vision, robotics, and medical imaging. This advanced graduate course aims to provide a holistic view of the issues related to these models: We will start with the history of how we got here, and then delve into the latest success stories. We will then focus on the implications of these technologies: social harms, security risks, legal issues, and environmental impacts. The class ends with reflections on the future implications of this trajectory.Required Course Background: knowledge equivalent to EN.601.471 /671 or EN.601.465 /665; linear algebra and statistics. Prerequisite(s): EN.601.471 OR EN.601.671 OR EN.601.465 OR EN.601.665 EN.601.773. Machine Social Intelligence. 3 Credits. No other species possesses a social intelligence quite like that of humans. Our ability to understand one another’s minds and actions, and to interact with one another in rich and complex ways, is the basis for much of our success, from governments to symphonies to the scientific enterprise. This course will discuss the principles of human social cognition, how we can use machine learning and AI models to computationally capture these principles, how these principles can help us build human-level machine social intelligence, and how social intelligence can enable the engineering of AI systems that can understand and interact with humans safely and productively in real-world settings. In this seminar course, we will read and discuss literature that cover diverse topics on social intelligence in humans and machines. These include (but are not limited to) social perception, Theory of Mind, multi-agent planning, multi-agent communication, social learning, human-AI teaming, moral judgment, and value alignment.Required Course Background: Linear Algebra, Probability and Statistics, and Calculus; 601.475/675 Machine Learning or EN.601.464 /664 Artificial Intelligence or equivalent. EN.601.774. Theory of Replicable Machine Learning. 3 Credits. Replicability is vital to ensuring scientific conclusions are reliable, but failures of replicability have been a major issue in nearly all scientific areas of study, and machine learning is no exception. In this course, we will study replicability as a property of learning and other statistical algorithms, developing a theory of replicable learning. We will cover recent formalizations of replicability and their relationships to other common stability notions such as differential privacy and adaptive generalization. We will survey replicable algorithms for fundamental learning tasks, and discuss the limitations of replicable algorithms. If time permits, we will discuss replicability in other settings, such as reinforcement learning and clustering, or other useful and related stability notions such as list replicability and global stability.Required Course Background: EN.601.433 /633 Intro Algorithms or instructor permission. EN.601.778. Advanced Topics in Causal Inference. 3 Credits. This course will cover advanced topics on all areas of causal inference, including learning causal effects, path-specific effects, and optimal policies from data featuring biases induced by missing data, confounders, selection, and measurement error, techniques for generalizing findings to different populations, complex probabilistic models relevant for causal inference applications, learning causal structure from data, and inference under interference and network effects. The course will feature a final project which would involve either an applied data analysis problem (with a causal inference flavor), a literature review, or theoretical work. Recommended Course Background: EN.601.447 /677. Prerequisite(s): EN.601.477 OR EN.601.677 EN.601.779. Machine Learning: Advanced Topics. 3 Credits. This course will focus on recent advances in machine learning. Topics will vary from year to year. The course will be project focused and involve presenting and discussing recent research papers. EN.601.783. Vision as Bayesian Inference. 3 Credits. This is an advanced course on computer vision from a probabilistic and machine learning perspective. It covers techniques such as linear and non-linear filtering, geometry, energy function methods, markov random fields, conditional random fields, graphical models, probabilistic grammars, and deep neural networks. These are illustrated on a set of vision problems ranging from image segmentation, semantic segmentation, depth estimation, object recognition, object parsing, scene parsing, action recognition, and text captioning. Required course background: calculus, linear algebra ( AS.110.201 or equiv.), probability and statistics (AS.550.311 or equiv.), and the ability to program in Python and C++. EN.601.787. Advanced Machine Learning: Machine Learning for Trustworthy AI. 3 Credits. This course teaches advanced machine learning methods for the design, implementation, and deployment of trustworthy AI systems. The topics we will cover include but are not limit to different types of robust learning methods, fair learning methods, safe learning methods, and research frontiers in transparency, interpretability, privacy, sustainability, AI safety and ethics. Students will learn the state-of-the-art methods in lectures, understand the recent advances by critiquing research articles, and apply/innovate new machine learning methods in an application. There will be homework assignments and a course project. Recommended course background: EN.601.475 /675. Distribution Area: Engineering EN.601.788. Machine Learning for Healthcare. 3 Credits. This course surveys the technical and practical challenges of applying machine learning in healthcare, focusing on two themes: The first theme will cover applications of machine learning to a wide range of healthcare data modalities (e.g., medical imaging, structured health records, etc). Beyond reviewing specific modeling approaches, we will focus on navigating pitfalls in model development and evaluation that arise in a healthcare context. The second theme will cover methodological approaches to developing safe and effective machine learning systems in healthcare, including topics such as (but not limited to) causality, fairness, and distribution shift. This course is designed for students who have a solid existing background in machine learning, and who are interested in both the technical and practical nuances of applying machine learning in healthcare. Grading will be done on the basis of homework assignments as well as a final project.Required course background: EN.601.475 /675 Machine Learning or equivalent. EN.601.790. Advanced Human-Computer Interaction: Research Methods. 3 Credits. This course is specifically tailored for graduate students, especially PhD students, to provide them with a comprehensive review of Human-Computer Interaction (HCI) research. The course covers foundations and frontiers in HCI research. Core topics include interaction, social computing, and AI+HCI; breadth topics include collaboration, conversational interactions, ubiquitous and tangible computing, and accessibility. We will examine research methods, philosophies of research, and diverse ways of knowing to build foundational concepts and analytical skills for engaging in and understanding human-computer interaction research. Students will read research papers and methodological theory and engage in critical writing, group discussion, and oral presentations. Students will gain knowledge in learning and practicing commonly used methodologies in HCI, such as interview, field and lab experiment design, and qualitative and quantitative analysis methods.Required Course Background: 601.490/690 or permission. EN.601.792. Advanced Topics in Conversational User Interfaces. 3 Credits. As a critical component of human-computer interaction, conversational user interfaces (CUIs) have the potential to revolutionize the way we interact with technology. This course is designed for graduate students who want to gain a deeper understanding of CUIs and their real world applications. Throughout the course, students will explore cutting-edge research and methodologies for designing, implementing, and evaluating CUIs. Various forms of conversational interface will be covered, including chatbots, voice assistants, and multimodal dialogue systems. Coursework will include short open-ended assignments focused on applying methods learned in class, reading recent papers, and a course project. Required course background: EN.601.490 /690 Intro HCI or permission. Prerequisite(s): EN.601.490 OR EN.601.690 EN.601.794. Privacy Technology, Design, and Law. 3 Credits. Privacy has long been considered as a fundamental human right. Emerging technologies such as social media, smart grid, Internet of Things, drones, and self-driving cars have raised heightened privacy issues. Recent developments of regulations such as the European Union's General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) is also drawing increasing attention from technologists, policymakers, and the media. How to protect people's privacy is a key challenge of our time. This course provides an in-depth look into privacy, privacy laws and regulations, privacy-enhancing technologies and mechanisms, and privacy design. Privacy will be examined from historical, philosophical, cultural, legal, economic, behavioral, and technical perspectives. This course is designed primarily for graduate students who are interested in privacy and are from a wide range of disciplines such as information science, computer science and engineering, law, business, media studies, economics, politics, and psychology. Recommended course background: EN.601.443 /643 Security & Privacy or equivalent. EN.601.801. Computer Science Seminar. 1 Credit. Seminar presentations by leading researchers across the field of computer science. EN.601.803. Masters Research. 3 - 10 Credits. Permission required. Independent research for masters or pre-dissertation PhD students. EN.601.805. Graduate Independent Study. 1 - 3 Credits. Permission required. Individual study in an area of mutual interest to a graduate student and a faculty member in the department. EN.601.807. Teaching Practicum. 1 Credit. PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement.(grad students) Permission req'd. EN.601.809. PhD Research. 3 - 20 Credits. Independent research for PhD students. EN.601.810. Diversity and Inclusion in Computer Science and Engineering. 1 Credit. This reading seminar will focus on the question of diversity and inclusion in Computer Science (in particular) and engineering (in general). We aim to study the ways in which the curriculum, environment, and structure of computer science within academia perpetuates biases alienating female and minoritized students, and to explore possible approaches for diversifying our field. The seminar will meet on a weekly basis, readings will be assigned, and students will be expected to participate in the discussion. EN.601.811. Future Faculty: Preparing a New Generation of PIs for the Academic Job Search. 1 Credit. The goal of this seminar-style course is to prepare senior PhD students and postdocs in CS and robotics adjacent disciplines for the academic job market. At the end of the course sequence, it is expected that participants will 1) understand benefits and possible challenges of the academic career path, 2) be familiar with many aspects of the academic job market (such as timing, required documents, interview schedule, …), 3) have completed a first draft of their application documents to be further refined with their respective advisors and mentors, 4) be prepared to tackle phone and on-campus interviews, and 5) have an appreciation for the essential tasks junior faculty must master quickly. EN.601.817. Selected Topics in Systems Research. 1 Credit. This course covers latest advances in the research of computer systems including operating systems, distributed system, mobile and cloud computing. Students will read and discuss recent research papers in top systems conferences. Each week, one student will present the paper and lead the discussion for the week. The focus topics covered in the papers vary semester to semester. Example topics include fault-tolerance, reliability, verification, energy efficiency, and virtualization. EN.601.819. Selected Topics in Cloud Computing and Networked Systems. 1 Credit. Participants will read and discuss seminal and recent foundational research on cloud and networked systems. EN.601.826. Selected Topics in Programming Languages. 1 Credit. This seminar course covers recent developments in the foundations of programming language design and implementation. Topics covered include type theory, process algebra, higher-order program analysis, and constraint systems. Students will be expected to present papers orally. EN.601.831. CS Theory Seminar. 1 Credit. Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers. EN.601.849. Selected Topics in Computational Immunogenomics. 1 Credit. Immunology studies defensive mechanisms of living organisms against external threats. Computational immunogenomics is a new branch of bioinformatics that develops and applies computational approaches to the study and interpretation of immunological data, seeking to answer questions about human adaptive immune responses to various pathogens, including but not limited to flu, HIV, and SARS-CoV-2. In this course, students will attend lectures and present immunogenomics papers in a journal club format. EN.601.856. Seminar: Medical Image Analysis. 1 Credit. This weekly seminar will focus on research issues in medical image analysis, including imagesegmentation, registration, statistical modeling, and applications. It will also include selected topicsrelating to medical image acquisition, especially where they relate to analysis. The purpose of thecourse is to provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups withinthe University. The format of the course is informal. Students will read selected papers. All students will be assumed to have read these papers by the time the paper is scheduled for discussion. But individual students will be assigned on a rotating basis to lead the discussion on particular papers or sections of papers. Co-listed with En.520.746. EN.601.857. Selected Topics in Computer Graphics. 1 Credit. In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. EN.601.862. Selected Topics in Medical Image Processing. 1 Credit. This course will provide a background in medical imaging modalities and the unique aspects of image processing as it pertains to medical imaging. We will cover what an image is, how it is formed through six imaging modalities, and how images are typically stored, as well as background topics such as image metrics, quantification, filtering and transforms. More advanced topics will be discussed such as visualization, image enhancement, segmentation and registration. The final few weeks will introduce the topic of neural networks in image processing. Students will be expected to read and discuss publications, as well as complete an implementation project and report. Recommended course background: programming & linear algebra. EN.601.864. Selected Topics in Multilingual Natural Language Processing. 1 Credit. This is a weekly reading group focused on Natural Language Processing (NLP) outside of English. Whereas methods have gotten very strong in recent years on English NLP tasks, many methods fail on other languages due to both linguistic differences as well as lack of available annotated resources. This course will focus on Cross-Language Information Retrieval, Cross-Lingual Information Extraction, Multilingual Semantics, Massively Multilingual Language Modeling, and other non-English NLP sub-fields. Students will be expected to read, discuss, and present papers. Required course background: EN.601.465 /665. EN.601.865. Selected Topics in Natural Language Processing. 1 Credit. A reading group exploring important current research in the field and potentially relevant material from related fields. In addition to reading and discussing each week's paper, enrolled students are expected to take turns selecting papers and leading the discussion. Prerequisite(s): EN.601.465 OR EN.601.665 . EN.601.866. Selected Topics in Computational Semantics. 1 Credit. This weekly reading group will review current research and survey articles on the topics of computational semantics, statistical machine translation, and natural language generation. Enrolled students will present papers and lead discussions. EN.601.867. Selected Topics in Trustworthy & Responsible Natural Language Processing. 1 Credit. This is a graduate student seminar aimed at introducing graduate students to the research areas of trustworthy and responsible NLP. This is primarily targeted at students with an NLP background and no or very little background in the course topics: bias, privacy, safety, misinformation, explainability, interpretability, robustness. Students will be expected to read, present and discuss papers each week. Required course background: NLP experience (eg. at least one of 601.665/667/668/671/765, etc.). EN.601.868. Selected Topics in Machine Translation. 1 Credit. Students in this course will review, present, and discuss current research in machine translation. Permission of instructor. EN.601.888. Translate Health AI Seminar. 1 Credit. Long title: Taking AI for healthcare from the GPU to the patient (THAI seminar).This seminar will cover various topics related to clinical translation of AI technologies in healthcare. Translation of AI for healthcare requires synergy across multiple domains including engineering, epidemiology and statistics, regulation, and clinical research methods. This series will include talks by faculty, invited speakers, and students on topics such as validation, model evaluation metrics, regulation, clinical evaluation including human machine interaction, studies on effectiveness and post-deployment real-world effects.
Computer Science
School of Engineering
Computing has grown to be pervasive throughout engineering, science, business, society, and entertainment. Computer Science at Johns Hopkins University (CS@JHU) is a diverse, collaborative, and intensely research-focused department. Our mission in the university is to enhance discovery and innovation in science, engineering and society through computing research and education:
Systems Engineering, Master of Science
School of Engineering
...Fall semester - EN.560.691 CaSE Graduate Seminar and/or Spring Semester - EN.560.692...