|
500.112 (E) |
GATEWAY COMPUTING: JAVA (3) staff This course introduces fundamental programming concepts and techniques, and is intended for all who plan to develop computational artifacts or intelligently deploy computational tools in their studies and careers. Topics covered include the design and implementation of algorithms using variables, control structures, arrays, functions, files, testing, debugging, and structured program design. Elements of object-oriented programming, algorithmic efficiency and data visualization are also introduced. Students deploy programming to develop working solutions that address problems in engineering, science and other areas of contemporary interest that vary from section to section. Course homework involves significant programming. Attendance and participation in class sessions are expected. |
MWF 50 minutes, limit 19/section |
|
601.104 (H) |
COMPUTER ETHICS (1) Leschke 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. |
Sections meet during the first 8 weeks of the semester
only.
Sec 01: Mon 4:30-6:00p |
|
601.124 (EH) |
THE ETHICS OF ARTIFICIAL INTELLIGENCE & AUTOMATION (3) Lopez-Gonzalez 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. |
Sec 01: MW 12-1:15p |
|
601.220 (E)
|
INTERMEDIATE PROGRAMMING (4) staff 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. The course includes usage of Linux tools for file manipulation, editing and programming, and git for versioning. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages. Prereq: 500.132/133/134 OR (C+/S*/S** or better grade in 500.112/113/114) or AP CS or equivalent. |
Sec 01 (Darvish): MWF 10:00-11:15a, limit 33 |
|
601.226 (EQ) |
DATA STRUCTURES (4) Presler-Marshall 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. Prereq: 500.132 OR (C+/S*/S** or better grade in 500.112 or 601.220) or AP CS credit or equivalent. |
Sec 01: MWF 12-1:15p |
|
601.229 (E) |
COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer 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. Prereq: 601.220. |
MWF 10a, limit 130 |
|
601.230 (EQ) |
MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE (4) More 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. Prereq: Gateway Computing (500.112/113/114/132/133/134 or AP CS or 601.220). Students can get credit for at most one of EN.601.230 or EN.601.231. |
Sec 01: MWF 9-9:50a, W 2-2:50p |
|
601.257 (E) |
COMPUTER GRAPHICS & 3D GAME PROGRAMMING (3) Simari 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. Prereq: 601.220, 601.226 and linear algebra. |
MWF 10 |
|
601.264 (E) |
PRACTICAL GENERATIVE AI (3) Madooei 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. Prereq: 601.220, 601.226 and 601.280 required; 601.290 or 601.490 recommended. Students may not register if they have already taken EN.601.470/670 or EN.601.471/671. |
MWF 11 |
|
601.290 (E) |
USER INTERFACES AND MOBILE APPLICATIONS (3) Selinski 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. Prereq: 601.220 and 601.226. |
MWF 3-4:15p |
|
601.404 (E) |
BRAIN & COMPUTATION (1) Kosaraju 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. Prerequisites: linear algebra, differential equations, probability, and algorithms; or instructor approval. Students can receive credit for EN.601.404 or EN.601.604, but not both. |
Tu 4:30-5:20p |
|
601.411 (E) |
CS INNOVATION AND ENTREPRENEURSHIP II (3) Dahbura & Aronhime This course is the second half of a two-course sequence and is a continuation of course 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: 660.410. |
MW 12-1:15p |
|
601.413 (E) |
SOFTWARE DEFINED NETWORKS (3) Sabnani
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. Prerequisite: EN.601.414/614. Students can receive credit for EN.601.413 or EN.601.613, but not both. |
Tu 4:30-7p |
|
601.414 (E) |
COMPUTER NETWORKS (3) Zhao 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. Prerequisites: EN.601.226 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
TuTh 1:30-2:45p |
|
601.418 (E) |
OPERATING SYSTEMS (3) Hovemeyer This course covers the 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. Prereq: 601.226 & 601.229. Students may receive credit for only one of 601.318/418/618. |
MW 12-1:15p |
|
601.421 (E) |
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Madooei 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. Prereq: 601.226 & 601.220 & (EN.601.280 or EN.601.290 or permission). Students may receive credit for only one of 601.421/621. |
MWF 3-3:50p |
|
601.422 (E) |
SOFTWARE TESTING & DEBUGGING (3) Darvish 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. Pre-req: EN.601.290 or EN.601.421. Students may receive credit for 601.422 or 601.622, but not both. |
MWF 1:30-2:20p |
|
601.425 (E) |
SOFTWARE SYSTEM DESIGN (3) Madooei 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. Prereq: 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. |
TuTh 12-1:15p |
|
601.426 (EQ) |
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith 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: 601.226. Freshmen and sophomores by permission only. |
MW 1:30-2:45 |
|
601.430 (EQ) |
COMBINATORICS AND GRAPH THEORY IN CS Li 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. Pre-requisite: 601.230 OR 550.171/553.171/553.172; probability theory and linear algebra recommended. Students may receive credit for only one of EN.601.430 and EN.601.630. |
TuTh 1:30-2:45pm |
|
601.433 (EQ) |
INTRO ALGORITHMS (3) Garg
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. Prereq: 601.226 and (553.171/172 or 601.231 or 601.230) or Perm. Req'd. Students may receive credit for only one of 601.433/633. |
TuTh 1:30-2:45p |
|
601.434 (EQ) |
RANDOMIZED & BIG DATA ALGORITHMS (3) Braverman The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered includee: tail inequalities, linear programming relaxation & randomized rounding, de-randomization, existence proofs, universal hashing, markov chains, metropolis and metropolis-hastings methods, mixing by coupling and by eigenvalues, counting problems, semi-definite programming and rounding, lower bound arguments, and applications of expanders. Prereq: 601.433/633 and (553.211 or 553.310/553.311 or 553.420/421 or equivalent). Students may receive credit for only one of 601.434/634. |
TuTh 12-1:15pm |
|
601.438 (EQ) |
ALGORITHMIC FOUNDATIONS OF DIFFERENTIAL PRIVACY (3) Dinitz 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. Prereq: 601.433/633 or permission. Students may receive credit for only one of 601.438/638. |
TuTh 9-10:15 |
|
601.444 (E)
|
MEDICAL DEVICE CYBERSECURITY Rushanan 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. Prereq: EN.601.443/643 or permission. |
TuTh 12-1:15 |
|
601.445 (E)
|
PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green 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. Prereq: 601.226 and 601.229. Students may receive credit for only one of 601.445/645. |
MW 3-4:15 |
|
601.446 (E) |
SKETCHING & INDEXING FOR SEQUENCES (3) Langmead 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. Pre-req: 600/601.226. Students may receive credit for 601.446 or 601.646, but not both. |
TuTh 9:00-10:15 |
|
601.456 (E) |
COMPUTER INTEGRATED SURGERY II (3) Taylor 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 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 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 601.356.
Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of
601.456, 601.496, 601.656. |
TuTh 1:30-2:45 |
601.457 (EQ) |
COMPUTER GRAPHICS (3) Kazhdan This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. Prereq: no audits; 601.220 & 601.226 & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.
|
MWF 11 |
|
601.461 (EQ) |
COMPUTER VISION (3) Katyal This course gives 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. Edge detection and color perception are covered as well. Elements of machine vision and biological vision are also included. (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761. |
Mon 4:30-7p |
|
601.463 (E) |
ALGORITHMS FOR SENSOR-BASED ROBOTICS (3) Leonard 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. Prereq: 601.226, linear algebra, calculus, probability. Students may receive credit for only one of 601.463/663. |
TuTh 12-1:15 |
|
601.464 (E) |
ARTIFICIAL INTELLIGENCE (3) Haque The course situates the study of Artificial Intelligence (AI) first in the broader context of Philosophy of Mind and Cognitive Psychology and then treats in-depth methods for automated reasoning, automatic problem solvers and planners, knowledge representation mechanisms, game playing, machine learning, and statistical pattern recognition. The class is a recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as deduction, learning, and planning and navigation. Strong programming skills and a good grasp of the English language are expected; students will be asked to complete both programming assignments and writing assignments. The course will include a brief introduction to scientific writing and experimental design, including assignments to apply these concepts. Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 3:00-4:15p |
|
601.466 (E) |
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky 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. Required course background: 601.226. |
TuTh 3-4:15 |
|
601.471 (E) |
NLP: SELF-SUPERVISED MODELS (3) Khashabi 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 may receive credit for EN.601.471 or EN.601.671, but not both. Pre-reqs: EN.601.226, one of (EN.601.465/665, EN.601.468/668, EN.601.475/675, EN.601.482/682), Linear Algebra, and Probability, as well as familiarity with Python/PyTorch. |
TuTh 9-10:15a |
|
601.472 (E) |
NATURAL LANGUAGE PROCESSING FOR COMPUTATIONAL SOCIAL SCIENCE (3) Field [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. Pre-reqs: one of (EN.601.465/665, EN.601.467/667, EN.601.468/668) and familiarity with Python/PyTorch. Students may receive credit for EN.601.472 or EN.601.672, but not both. |
MW 1:30-2:45p |
|
601.475
(E) |
MACHINE LEARNING (3) Arora The goal of machine learning (a subfield of artificial intelligence) is the development of 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 and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include both a heavy programming components as well as analytical questions that explore various machine learning concepts. This class will build on prerequisites that include probability, linear algebra, multivariate calculus and basic optimization. Pre-reqs: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Students may receive credit for only one of 601.475/675. |
MWF 12-1:15p |
|
601.482 (E) |
MACHINE LEARNING: DEEP LEARNING (4) Nalisnick
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. Pre-req: EN.601.226 and (AS.110.201 or AS.110.212 or EN.553.291) and (EN.553.310 or EN.553.311 or EN.553.420 or EN.560.348) and Calc III; numerical optimization and Python recommended. |
MW 1:30-2:45p, F 1:30-2:20p |
|
EN.601.484 (E) |
EXPLAINABLE AI DESIGN & HUMAN-AI INTERACTION (3) Unberath
(was ML: Interpretable Machine Learning Design, revised
description below) Pre-reqs: 601.475/675 or 601.464/664 or 601.482/682; coding in Python/PyTorch. Recommended (601.454/654, 601.290, 601.490/690 or 601.491/691) and 601.477/677. Students may receive credit for only one of 601.484/684. |
MW 4:30-5:45 |
|
EN.601.487 (E) |
ML: COPING WITH NON-STATIONARY ENVIRONMENTS AND ERRORS (3) Liu 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. Pre-req: 601.475/675. Students may receive credit for only one of 601.487/687, and may not take this course after taking EN.601.787. |
MW 3-4:15p |
|
601.490 (E) |
INTRO TO HUMAN-COMPUTER INTERACTION (3) Reiter 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. Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01: Mon 4:30-7p |
|
601.491 (E) |
HUMAN-ROBOT INTERACTION (3) staff 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. Pre-requisite: EN.601.220 and EN.601.226. |
TuTh 3-4:15 |
|
601.496 (E) |
COMPUTER INTEGRATED SURGERY II - TEAMS (3) Taylor 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.
Prereq: 601.455/655 or perm req'd. Students may receive credit for
only one of 601.456, 601.496, 601.656. |
TuTh 1:30-2:45 |
|
601.501 |
COMPUTER SCIENCE WORKSHOP An independent 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. Permission of faculty sponsor is required. |
See below for faculty section numbers. |
|
601.503 |
UNDERGRADUATE INDEPENDENT STUDY Individual guided study for undergraduates, 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 of faculty sponsor is required.
|
See below for faculty section numbers. |
|
601.507 |
UNDERGRADUATE RESEARCH Independent 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. |
See below for faculty section numbers and whether to select 507 or 517. |
|
601.509 |
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit, which is the limit per semester. Permission of faculty sponsor is required. |
See below for faculty section numbers. |
|
601.513 |
GROUP UNDERGRADUATE PROJECT 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. Permission required. |
Only for faculty specifically marked below. |
|
601.517 |
GROUP UNDERGRADUATE RESEARCH 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. Permission required. |
Only for faculty specifically marked below. |
|
601.520 |
SENIOR HONORS THESIS (3) For computer science majors only, a continuation of 601.519. Prerequisite: 601.519 |
See below for faculty section numbers. |
|
601.604 (E) |
BRAIN & COMPUTATION (1) Kosaraju 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. Required course background: linear algebra, differential equations, probability, and algorithms; or instructor approval. Students can receive credit for EN.601.404 or EN.601.604, but not both. |
Tu 4:30-5:20p |
|
601.613 (E) |
SOFTWARE DEFINED NETWORKS (3) Sabnani 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. Students can receive credit for EN.601.413 or EN.601.613, but not both. |
Tu 4:30-7p |
|
601.614 |
COMPUTER NETWORKS Zhao Same as 601.414, for graduate students. Required course background: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
TuTh 1:30-2:45p |
|
601.618 |
OPERATING SYSTEMS Hovemeyer Same material as 601.418, for graduate students. Required course background: Data Structures & Computer System Fundamentals. Students may receive credit for only one of 601.318/418/618. |
MW 12-1:15p |
|
601.621 |
OBJECT ORIENTED SOFTWARE ENGINEERING Madooei 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. |
MWF 3-3:50p |
|
601.622 |
SOFTWARE TESTING & DEBUGGING (3) Darvish 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. Students may receive credit for 601.422 or 601.622, but not both. |
MWF 1:30-2:20p |
|
601.625 |
SOFTWARE SYSTEM DESIGN (3) Madooei 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. |
TuTh 12-1:15p |
|
601.626 |
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Same as 601.426, for graduate stuents. Students may receive credit for only one of 601.426/626. Required course background: 601.226. |
MW 1:30-2:45 |
|
601.630 |
COMBINATORICS AND GRAPH THEORY IN CS Li 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 Background: discrete math; probability theory and linear algebra recommended. Student may receive credit for only one of 601.430/601.630. |
TuTh 1:30-2:45p |
|
601.633 |
INTRO ALGORITHMS Garg
Same as 601.433, for graduate students. Required Background: data structures, discrete math, proof writing. Students may receive credit for only one of 601.433/633. |
TuTh 1:30-2:45p |
|
601.634 |
RANDOMIZED & BIG DATA ALGORITHMS Braverman Same material as 601.434, for graduate students. Required Background: Algorithms and probability. Students may receive credit for only one of 601.434/634. |
TuTh 12-1:15p
|
|
601.638 (EQ) |
ALGORITHMIC FOUNDATIONS OF DIFFERENTIAL PRIVACY (3) Dinitz 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. |
TuTh 9-10:15 |
|
601.644 (E)
|
MEDICAL DEVICE CYBERSECURITY Rushanan 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. Prereq: EN.601.443/643 or permission. |
TuTh 12-1:15 |
|
601.645
|
PRACTICAL CRYPTOGRAPHIC SYSTEMS Green Same material as 601.445, for graduate students. Required Course Background: knowledge of data structures and computer system fundamentals. Students may receive credit for only one of 601.445/645. |
MW 3-4:15 |
|
601.646 |
SKETCHING & INDEXING FOR SEQUENCES (3) Langmead 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. Pre-req: Data Structures. Students may receive credit for 601.446 or 601.646, but not both. |
TuTh 9-10:15 |
|
601.656 |
COMPUTER INTEGRATED SURGERY II Taylor Same as 601.456, for graduate students. Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of 601.456/496/656. |
TuTh 1:30-2:45 |
601.657 |
COMPUTER GRAPHICS Kazhdan Same material as 601.457, for graduate students. Prereq: no audits; Intermediate Programming (C/C++) & Data Structures & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.
|
MWF 11 |
|
601.661 |
COMPUTER VISION Katyal Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming & linear algebra & prob/stat |
Mon 4:30-7p |
|
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same as 601.463, for graduate students. Required course background: 601.226, linear algebra, calculus, probability. Students may receive credit for only one of 601.463/663. |
TuTh 12-1:15 |
|
601.664 |
ARTIFICIAL INTELLIGENCE Koehn Same as 601.464, for graduate students. Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 1:30-2:45p |
|
601.666 |
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky Same material as 601.466, for graduate students. Students may receive credit for at most one of 601.466/666. Required course background: 601.226. |
TuTh 3-4:15p |
|
601.671 (E) |
NLP: SELF-SUPERVISED MODELS (3) Khashabi 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 may receive credit for EN.601.471 or EN.601.671, but not both. Required course background: data structures, linear algebra, probability, familiarity with Python/PyTorch, natural language processing or machine learning. Pre-reqs: one of EN.601.464/664, EN.601.465/665, EN.601.467/667, EN.601.468/668, EN.601.475/675. |
TuTh 9-10:15a |
|
601.672 |
NATURAL LANGUAGE PROCESSING FOR COMPUTATIONAL SOCIAL SCIENCE (3) Field [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. |
MW 1:30-2:45p |
|
601.675 |
MACHINE LEARNING Arora Same as 601.475, for graduate students. Required course background: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Students may receive credit for only one of 601.475/675. |
MWF 12-1:15p |
|
601.682 |
MACHINE LEARNING: DEEP LEARNING Nalisnick
Same as 601.482, for graduate students.
Required course background: data structures, probability and linear algebra; numerical optimization and Python recommended. |
MW 1:30-2:45p, F 1:30-2:20p |
|
EN.601.684 (E) |
EXPLAINABLE AI DESIGN & HUMAN-AI INTERACTION (3) Unberath
(was ML: Interpretable Machine Learning Design, revised
description below) Required course background: 601.475/675 or 601.464/664 or 601.482/682; coding in Python/PyTorch. Recommended (601.454/654, 601.290, 601.490/690 or 601.491/691) and 601.477/677. Students may receive credit for only one of 601.484/684. |
MW 4:30-5:45 |
|
EN.601.687 |
ML: COPING WITH NON-STATIONARY ENVIRONMENTS AND ERRORS (3) Liu 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. |
MW 3-4:15p |
|
601.690 |
INTRO TO HUMAN-COMPUTER INTERACTION Reiter Same material as EN.601.490, for graduate students. Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01: Mon 4:30-7p Sec 02: Wed 4:30-7p Sec 03: Mon 4:30-7p Sec 04: Wed 4:30-7p |
|
601.691 |
HUMAN-ROBOT INTERACTION staff 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 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. Required course background: EN.601.220 and EN.601.226. |
TuTh 3-4:15 |
|
601.714 |
ADVANCED COMPUTER NETWORKS Ghorbani
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. |
W 4:30-7pm |
|
601.715 |
ADVANCED NETWORKS: INTERNET MEASUREMENT Marder
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. |
TuTh 9-10:15a |
|
601.773 (E) |
MACHINE SOCIAL INTELLIGENCE (3) Shu 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. |
TuTh 1:30-2:45p |
|
601.774 |
THEORY OF REPLICABLE MACHINE LEARNING Sorrell 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. |
TuTh 3-4:15p |
|
601.783 |
VISION AS BAYESIAN INFERENCE (3) Yuille 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++. Background in computer vision (EN.601.461/661) and machine learning (EN.601.475/675) suggested but not required. |
TuTh 9-10:15a |
|
601.790 |
ADVANCED HCI: RESEARCH METHODS Xiao 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. |
MW 3-4:15p |
|
601.801 |
Required for all CS PhD students. Strongly recommended for MSE students. Only 1st & 2nd year PhD students should formally register. |
TuTh 10:30-11:45 |
|
601.803 |
MASTERS RESEARCH Independent research for masters or pre-dissertation PhD students. Permission required. |
See below for faculty section numbers. |
|
601.805 |
GRADUATE INDEPENDENT STUDY Permission Required. |
See below for faculty section numbers. |
|
601.807 |
TEACHING PRACTICUM Selinski/Smith PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement. Permission required. |
limit 30, P/F only |
|
601.809 |
PHD RESEARCH |
See below for faculty section numbers. |
|
601.810 |
DIVERSITY & INCLUSION IN COMPUTER SCIENCE & ENGINEERING Kazhdan 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. |
Wed 4:30p |
|
601.826 |
SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith 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. |
Fri 10 |
|
601.831 |
CS THEORY SEMINAR Dinitz, Li 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. |
W 12 |
|
601.856 |
SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince This weekly seminar will focus on research issues in medical image analysis, including image segmentation, registration, statistical modeling, and applications. It will also include selected topics relating to medical image acquisition, especially where they relate to analysis. The purpose of the course 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 within the 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 520.746. |
Tu 3-4:15 |
|
601.857 | SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan 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. |
M 1p |
|
601.864 |
SELECTED TOPICS IN MULTILINGUAL NATURAL LANGUAGE PROCESSING Yarowsky 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. |
Th 12-1p |
|
601.865 |
SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Eisner
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. |
Wed |
|
601.866 |
SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme A seminar focussed on current research and survey articles on computational semantics. |
Fr 10-10:50 |
|
601.868 |
SELECTED TOPICS IN MACHINE TRANSLATION Koehn Students in this course will review, present, and discuss current research in machine translation. Prereq: permission of instructor. |
M 11-12 |
|
620.745 |
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Cowan, Whitcomb, Vidal, Etienne-Cummings Seminar series in robotics. Topics include: Medical robotics, including computer-integrated surgical systems and image-guided intervention. Sensor based robotics, including computer vision and biomedical image analysis. Algorithmic robotics, robot control and machine learning. Autonomous robotics for monitoring, exploration and manipulation with applications in home, environmental (land, sea, space), and defense areas. Biorobotics and neuromechanics, including devices, algorithms and approaches to robotics inspired by principles in biomechanics and neuroscience. Human-machine systems, including haptic and visual feedback, human perception, cognition and decision making, and human-machine collaborative systems. Cross-listed with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. |
Wed 12-1:15p |
|
520.807 |
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Khudanpur CLSP seminar series, for any students interested in current topics in language and speech processing. |
Mon & Fri 12-1:15 |
01 - Xin Li 02 - Rao Kosaraju (emeritus) 03 - Soudeh Ghorbani 04 - Russ Taylor (ugrad research use 517, not 507) 05 - Scott Smith (use 513 for group project development) 06 - Joanne Selinski (use 513 for group project development) 07 - Michael Oberst 08 - Ali Madooei (use 513 for group project development) 09 - Greg Hager (ugrad research use 517, not 507) 10 - Craig Jones 11 - Sanjeev Khudhanpur [ECE] 12 - Yair Amir (emeritus) 13 - David Yarowsky 14 - Noah Cowan 15 - Randal Burns 16 - Jason Eisner (ugrad research use 517, not 507) 17 - Mark Dredze 18 - Michael Dinitz 19 - Rachel Karchin [BME] 20 - Michael Schatz 21 - Avi Rubin (emeritus) 22 - Matt Green 23 - Yinzhi Cao 24 - Raman Arora (ugrad research use 517, not 507) 25 - Najim Dehak [ECE] 26 - Misha Kazhdan 27 - David Hovemeyer (use 513 for group project development) 28 - Ali Darvish (use 513 for group project development) 29 - Alex Szalay [Physics] 30 - Peter Kazanzides 31 - Jerry Prince [BME] 32 - Carey Priebe [AMS] 33 - Anjalie Field 34 - Rene Vidal [BME] 35 - Alexis Battle (ugrad research use 517, not 507) [BME] 36 - Emad Boctor (ugrad research use 517, not 507) [SOM] 37 - Mathias Unberath 38 - Ben VanDurme 39 - Jeff Siewerdsen 40 - Vladimir Braverman 41 - Suchi Saria 42 - Ben Langmead 43 - Steven Salzberg 44 - Jean Fan [BME] 45 - Liliana Florea [SOM] 46 - Casey Overby Taylor [SPH] 47 - Philipp Koehn 48 - Abhishek Jain 49 - Anton Dahbura (ugrad research use 517, or 513 for group project development) 50 - Joshua Vogelstein [BME] 51 - Ilya Shpitser 52 - Jessica Sorrell 53 - Tamas Budavari [AMS] 54 - Alan Yuille 55 - Peng Ryan Huang 56 - Kai Presler-Marshall 57 - Chien-Ming Huang 58 - Will Gray Roncal (ugrad research use 517, not 507) 59 - Kevin Duh [CLSP] 60 - Mihaela Pertea [BME] 61 - Archana Venkataraman [ECE] 62 - Matt Post [CLSP] 63 - Vishal Patel [ECE] 64 - Rama Chellappa [ECE] 65 - Mehran Armand [MechE] 66 - Jeremias Sulam [BME] 67 - Anqi Liu 68 - Yana Safanova 69 - Musad Haque 70 - Eric Nalisnik 71 - Amitabh Basu [AMS] 72 - Thomas Lippincott [CLSP] 73 - Joel Bader [BME] 74 - Daniel Khashabi 75 - Nicolas Loizou [AMS] 76 - Alejandro Martin Gomez 77 - Kenton Murray 78 - Gillian Hadfield 79 - Krishan Sabnani 80 - Nicholas Andrews [HTLCOE] 81 - Muyinatu (Bisi) Bell [ECE] 82 - Ziang Xiao 83 - Renjie Zhao 84 - Alex Marder 85 - Tianmin Shu 86 - Dawn Lawrie [HLTCOE] 87 - Kimia Ghobadi [CaSE]