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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 |
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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. |
Sec 01: Wed 4:30-6:20p, alternate weeks (start 1/26)
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601.220 (E)
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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 (Hovermeyer): MWF 12:00-1:15 |
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601.226 (EQ) |
DATA STRUCTURES (4) Madooei 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 or equivalent. |
Sec 01: MWF 12-1:15 |
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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. |
Sec 01: MWF 9 |
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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. Pre/co-req: 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 9-9:50, W 4:30-5:20 |
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601.231 (EQ) |
AUTOMATA & COMPUTATION THEORY (3) Li 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. Students may not receive credit for 600.271/601.231 and 600.471/601.631 for the same degree. Prereq: 553.171/172 or 601.230. Students can get credit for at most one of EN.601.230 or EN.601.231. |
TuTh 1:30-2:45
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601.280 (E) |
FULL-STACK JAVASCRIPT (3) Cao 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. Prereq: 601.220 or 601.226. Students must not have taken or be concurrently enrolled in 601.421/621 OOSE. |
TuTh 12-1:15 |
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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. [Oral] Prereq: 601.220 and 601.226. |
MW 3-4:15 |
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601.315 (E) |
DATABASES (3) More 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. [Systems] Prereq: 600/601.226. Students may receive credit for only one of 601.315/415/615. Graduate students not permitted. |
TuTh 9-10:15 |
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601.350 (E) |
GENOMIC DATA SCIENCE (3) Salzberg [Formerly Intro to Genomic Research] 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 literature by reading high-profile research papers that generated groundbreaking or controversial results. [Applications] Prerequisites: knowledge of the Unix operating system and programming expertise in a language such as Perl or Python.
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TuTh 3-4:15 |
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601.356 (E) |
COMPUTER INTEGRATED SURGERY SEMINAR (1) Taylor Lecture only version of 601.456 (no project). Prereq: 601.455 or perm req'd. Students may receive credit for 600.356 or 600.456, but not both. |
TuTh 1:30-2:45 |
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601.402 (E) |
DIGITAL HEALTH AND BIOMEDICAL INFORMATICS (1) Lehmann 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. Prerequisite: none. Short course meets 5 weeks: Jan 25 - Feb 24 |
TuTh 4:30-5:45 |
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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. [Oral] Prerequisite: 660.410. |
MW 3-4:15 |
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601.414 (E) |
COMPUTER NETWORKS (3) Rubin/Jois 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. [Systems] Prerequisites: EN.601.226 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
Sec 01 (Rubin): TuTh 9-10:15 |
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601.419 (E) |
CLOUD COMPUTING (3) Ghorbani 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. [Systems] Prerequisites: EN.601.226 and EN.601.414 or permission. Students can only receive credit for one of 601.419/619. |
MW 3-4:15 |
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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. [(Systems or Applications), Oral] Prereq: 601.226 & 601.220 & (EN.601.280 or EN.601.290). Students may receive credit for only one of 601.421/621. |
MWF 4:30-5:20p |
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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. [Systems] Pre-req: EN.601.290 or EN.601.421. Students may receive credit for 601.422 or 601.622, but not both. |
TuTh 1:30-2:45 |
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601.424 (E) |
RELIABLE SOFTWARE SYSTEMS (3) R. Huang 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. [Systems or Analysis]
Pre-req: 601.220 and 601.328/428/628. Students may receive credit for 601.424 or 601.624, but not both. |
MW 1:30-2:45 |
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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. [Analysis] Required course background: 601.226. Freshmen and sophomores by permission only. |
MW 1:30-2:45 |
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601.433 (EQ) |
INTRO ALGORITHMS (3) Braverman
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. [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 12-1:15 |
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601.436 (EQ) |
ALGORITHMIC GAME THEORY Dinitz 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. Students may receive credit for 601.436 or 601.636, but not both. [Analysis] Prereq: 601.433/633 or permission. |
TuTh 4:30-5:45 |
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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. [Analysis] Pre-req: 600/601.226. Students may receive credit for 601.446 or 601.646, but not both. |
TuTh 9:00-10:15 |
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601.454 (E) CSCI-APPL |
AUGMENTED REALITY (3) Navab & Azimi
Same as 601.654, for undergraduate students.
[Applications] Students may receive credit for only one of 601.454/654. Prerequisites: EN.601.220, EN.601.226, linear algebra. |
MW 8:30-9:45a (was TuTh 9-10:15a) |
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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. [Applications, Oral]
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 |
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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. [Applications, Oral]
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 |
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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. [Analysis] Prereq: 601.226, linear algebra, calculus, probability. Students may receive credit for only one of 601.463/663. |
TuTh 12-1:15 |
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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. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 10:30-11:45 |
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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. [Applications] Required course background: 601.226. |
TuTh 3-4:15 |
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601.475 (E) |
MACHINE LEARNING (3) Dredze
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.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus, probability, linear algebra. |
MWF 1:30-2:45p |
|
601.482 (E) |
MACHINE LEARNING: DEEP LEARNING (4) Unberath
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 EN.553.311 or EN.553.420 or EN.560.348); numerical optimization and Python recommended. |
Mon 8:30-9:45a lectures |
|
601.491 (E) |
HUMAN-ROBOT INTERACTION (3) C. Huang 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. [Applications] Pre-requisite: EN.601.220 and EN.601.226. |
TuTh 3-4:15 |
|
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. |
|
601.517 |
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. |
|
601.611 |
CS INNOVATION AND ENTREPRENEURSHIP II Dahbura & Aronhime Graduate level version of EN.601.411 (see for description) Prerequisites: 660.410. |
MW 3-4:15p |
|
601.614 |
COMPUTER NETWORKS Rubin/Jois Same as 601.414, for graduate students. [Systems] Required course background: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
Sec 01 (Rubin): TuTh 9-10:15 |
|
601.619 |
CLOUD COMPUTING Ghorbani [Same as 601.419, for graduate students.] 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. [Systems] Required course background: EN.601.226 and EN.601.414/614 or permission. Students can only receive credit for one of 601.419/619. |
MW 3-4:15 |
|
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. [Systems or Applications] 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 4:30-5:20p |
|
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. [Systems] Pre-req: EN.601.290 or EN.601.421 or EN.601.621. Students may receive credit for 601.422 or 601.622, but not both. |
TuTh 1:30-2:45 |
|
601.624 |
RELIABLE SOFTWARE SYSTEMS (3) R. Huang 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. [Systems or Analysis]
Recommended course background: 601.220 and 601.328/428/628. Students may receive credit for 601.424 or 601.624, but not both. |
MW 1:30-2:45 |
|
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. [Analysis] Required course background: 601.226. |
MW 1:30-2:45 |
|
601.633 |
INTRO ALGORITHMS Braverman
Same as 601.433, for graduate students. [Analysis] Prereq: 601.226 and (553.171/172 or 601.230 or 601.231) or Perm. Required. Students may receive credit for only one of 601.433/633. |
TuTh 12-1:15 |
|
601.636 |
ALGORITHMIC GAME THEORY Dinitz Same as EN.601.436, for graduate students. Students may receive credit for 601.436 or 601.636, but not both. Pre-req: 601.433/633 or permission. [Analysis] Prereq: 601.433/633 or permission. |
TuTh 4:30-5:45 |
|
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. [Analysis] Pre-req: Data Structures. Students may receive credit for 601.446 or 601.646, but not both. |
TuTh 9-10:15 |
|
601.654 |
AUGMENTED REALITY (3) Navab & Azimi
This course introduces students to the field of Augmented
Reality. It reviews its basic definitions, principles and
applications. It then focuses on Medical Augmented Reality and its
particular requirements. The course also discusses the main issues of
calibration, tracking, multi-modal registration, advance visualization
and display technologies. Homework in this course will relate to the
mathematical methods used for calibration, tracking and visualization
in medical augmented reality. Students may also be asked to read
papers and implement various techniques within group
projects. [Applications] Students may receive credit for 600.484 or 600.684, but
not both. Required course background: intermediate programming (C/C++), data structures, linear algebra. |
MW 8:30-9:45a (was TuTh 9-10:15a) |
|
601.656 |
COMPUTER INTEGRATED SURGERY II Taylor Same as 601.456, for graduate students. [Applications] Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of 601.456/656. |
TuTh 1:30-2:45 |
|
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same as 601.463, for graduate students. [Analysis] 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 Haque Same as 601.464, for graduate students. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 10:30-11:45 |
|
601.666 |
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky Same material as 601.466, for graduate students. [Applications] Students may receive credit for at most one of 601.466/666. Required course background: 601.226. |
TuTh 3-4:15p |
|
601.675 |
MACHINE LEARNING Dredze
Same as 601.475, for graduate students.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus, probability, linear algebra. |
MWF 1:30-2:45 |
|
601.682 |
MACHINE LEARNING: DEEP LEARNING Unberath
Same as 601.482, for graduate students. [Applications]
Required course background: data structures, probability and linear algebra; numerical optimization and Python recommended. |
Sec 01, Mon 8:30-9:45: limit 50, CS & MSEM grads only until 11/30 |
|
601.691 |
HUMAN-ROBOT INTERACTION C. Huang 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. [Applications] Required course background: EN.601.220 and EN.601.226. |
TuTh 3-4:15 |
|
601.717 |
ADVANCED DISTRIBUTED SYSTEMS AND NETWORKS Amir 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. [Systems] Prereq: a systems course (distributed systems, operating systems, computer networks, parallel programming), or permission of instructor. |
TuTh 3-4:15 |
|
601.718 |
ADVANCED OPERATING SYSTEMS R. Huang Students will study advanced operating system topics and be exposed to recent developments in operating systems research. This course involves readings on classic and new papers. Topics include virtual memory management, synchronization and communication, file systems, protection and security, operating system structure and extension techniques, fault tolerance, and history and experience of systems programming. [Systems] Prereq: 600/601.318/418/618 or permission. |
MW 1:30-2:45 |
|
601.779 |
MACHINE LEARNING: ADVANCED TOPICS Arora 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. Pre-req: 600/601.475/675 or 600/601.775 or 600/601.479/679 or 600/601.476/676 or equiv. |
Fri 1:30-4p |
|
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. [Analysis or Applications] 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 9-10:15 |
|
601.787 |
ADVANCED MACHINE LEARNING: MACHINE LEARNING FOR TRUSTWORTHY AI Liu 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. Expected course background: 601.475/675 Machine Learning; recommended 601.476/676 ML: Data to Models and 601.482/682 Deep Learning. |
MW 3-4:15 |
|
601.801 |
Required for all CS PhD students. Strongly recommended for MSE students. |
TuTh 10:30-12 |
|
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 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 25 |
|
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. |
limit 8 |
|
601.817 |
SELECTED TOPICS IN SYSTEMS RESEARCH R.Huang 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. |
Fr 1-2:15 |
|
601.819 |
SELECTED TOPICS IN CLOUD COMPUTING AND NETWORKED SYSTEMS Ghorbani Participants will read and discuss seminal and recent foundational research on cloud and networked systems. |
W 4:30-5:45p |
|
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 11-12 |
|
601.831 |
CS THEORY SEMINAR [Braverman,] 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 |
|
601.833CANCELED |
SEMINAR IN ALGORITHMS Braverman This course will explore algorithms and theoretical computer science with a focus on algorithms for massive data. Examples of topics include streaming algorithms, approximation algorithms, online algorithms. Students will be encouraged to select a paper and lead a discussion. External speakers will be invited to present current work as well. This course is a good opportunity for motivated students to learn modern algorithmic methods. Prereq: 600.463 or equivalent. |
Th 1:30-2:30 |
|
601.849 | SELECTED TOPICS IN COMPUTATIONAL IMMUNOGENOMICS Safonova 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. |
Mon 10-10:50am |
|
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. |
Tu 3-4:15 |
|
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. Enrolled students are expected to present papers and lead discussion. Required course background: 600.465 or permission of instructor. |
Wed 12-1:15 |
|
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 |
|
500.745 |
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 12-1:30 |
|
520.702 |
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Trmal CLSP seminar series, for any students interested in current topics in language and speech processing. |
Mon & Fri 12-1:15 |
|
650.624 |
NETWORK SECURITY Johnston
This course focuses on communication security in computer systems and
networks. The course is intended to provide students with an
introduction to the field of network security. The course covers
network security services such as authentication and access control,
integrity and confidentiality of data, firewalls and related
technologies, Web security and privacy. Course work involves
implementing various security techniques. A course project is
required. |
TuTh 3-4:15p |
|
650.631 |
ETHICAL HACKING Watkins Cyber security affects every facet of industry and our government, and thus is now a threat to National Security. This course is designed to introduce students to the skills needed to defend computer network infrastructure by exposing them to the hands-on identification and exploitation of vulnerabilities in servers (i.e., Windows and Linux), wireless networks, websites, and cryptologic systems. These skills will be tested by having teams of students develop and participate in instructor lead capture-the-flag competitions. Also included are advanced topics such as shell coding, IDA Pro analysis, fuzzing, and writing or exploiting network-based applications or techniques such as web servers, spoofing, and denial of service. |
Th 4:30-7p |
|
650.640 |
MORAL & LEGAL FOUNDATIONS OF PRIVACY Galluzzo This course explores the ethical and legal underpinnings of the concept of privacy. It examines the nature and scope of the right to privacy by addressing fundamental questions such as: What is privacy? Why is privacy morally important? How is the right to privacy been articulated in constitutional law? |
Mon 4:30-6:45p |
|
650.654 |
COMPUTER INTRUSION DETECTION Li Intrusion detection supports the on-line monitoring of computer system activities and the detection of attempts to compromise normal services. This course starts with an overview of intrusion detection tasks and activities. Detailed discussion introduces a traditional classification of intrusion detection models, applications in host-centered and distributed environments, and various intrusion detection techniques ranging from statistical analysis to biological computing. This course serves as a comprehensive introduction of recent research efforts in intrusion detection and the challenges facing modern intrusion detection systems. Students will also be able to pursue in-depth study of special topics of interest in course projects. |
TuTh 12-1:15p |
|
650.672 |
SECURITY ANALYTICS Zhang
Security analytics refers to information technology solutions that
gather and analyze security events to bring situational awareness and
enable IT staff to understand and analyze events that pose the
greatest risk. Increasingly, detecting and preventing cyber attacks
require sophisticated use of data analytics and machine learning
tools. This course will cover fundamental theories and methods in data
science, modern security analytical tools, and practical use cases of
security analytics. Students of this course learn concepts, tasks, and
methods of data science; and how to apply data science to cyber
security problems. Students also learn how to use modern software in
security analytics. |
Fri 4:15 -6:45p |
|
650.683 |
CYBERSECURITY RISK MANAGEMENT staff
Data breaches, cyber attacks, cybercrime, and information operations
in social media continue to increase in frequency and severity,
causing businesses and governments to focus more resources on
cybersecurity risk management and compliance. Utilizing real-world
data breaches and attacks as motivation, this course will provide
students knowledge of risk management concepts, frameworks, compliance
regimes and best industry practices used to ensure sound cybersecurity
practices in government, commercial, and academic organizations. Lab
exercises will provide opportunities for students to experience key
aspects of the risk management process and help prepare them for
post-graduation assignments as cybersecurity professionals. |
TuTh 1:30-2:45p |
|
650.757 |
ADVANCED COMPUTER FORENSICS Leschke This course will analyze advanced topics and state of the art issues in the field of digital forensics. The course will be run in a research seminar format and students will be given both basic and applied research projects in such areas as: intrusion analysis, network forensics, memory forensics, mobile devices, and other emerging issues. |
Wed 6:40-9:10p |
|
650.837 |
INFORMATION SECURITY PROJECTS Dahbura & Li Open to MSSI students. Permission Required for non-MSSI students. All MSSI programs must include a project involving a research and development oriented investigation focused on an approved topic addressing the field of information security and assurance from the perspective of relevant applications and/or theory. There must be project supervision and approval involving a JHUISI affiliated faculty member. A project can be conducted individually or within a team-structured environment comprised of MSSI students and an advisor. A successful project must result in an associated report suitable for on-line distribution. When appropriate, a project can also lead to the development of a so-called "deliverable" such as software or a prototype system. Projects can be sponsored by government/industry partners and affiliates of the Information Security Institute, and can also be related to faculty research programs supported by grants and Contracts. Required for MSSI students on full-time status. |
MWF 10:00 |
|
650.840 |
INFORMATION SECURITY INDEPENDENT STUDY Li Individual study in an area of mutual interest to a graduate student and a faculty member in the Institute. |
01 - Xin Li 02 - Rao Kosaraju (emeritus) 03 - Soudeh Ghorbani 04 - Russ Taylor (ugrad research use 517, not 507) 05 - Scott Smith 06 - Joanne Selinski 07 - Harold Lehmann [SPH] 08 - Ali Madooei 09 - Greg Hager (ugrad research use 517, not 507) 10 - Gregory Chirikjian [MechE] 11 - Sanjeev Khudhanpur [ECE] 12 - Yair Amir 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 22 - Matt Green 23 - Yinzhi Cao 24 - Raman Arora (ugrad research use 517, not 507) 25 - Rai Winslow [BME] 26 - Misha Kazhdan 27 - David Hovemeyer 28 - Ali Darvish 29 - Alex Szalay [Physics] 30 - Peter Kazanzides 31 - Jerry Prince [BME] 32 - Carey Priebe [AMS] 33 - Nassir Navab 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, not 507) 50 - Joshua Vogelstein [BME] 51 - Ilya Shpitser 52 - Austin Reiter 53 - Tamas Budavari [AMS] 54 - Alan Yuille 55 - Peng Ryan Huang 56 - Xin Jin 57 - Chien-Ming Huang 58 - Will Gray Roncal (ugrad research use 517, not 507) 59 - Kevin Duh [CLSP] 60 - Mihaela Pertea 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 - Stephen Walli 71 - Gregory Falco [CaSE] 72 - Thomas Lippincott [CLSP] 73 - Joel Bader [BME]