Spring 2020 Courses

Below are the computer science course offerings for one semester. This list only includes courses that count without reservation towards CS program requirements. Undergraduate majors might also want to consult the list of non-department courses that may be used as "CS other" in accordance with established credit restrictions.

  • See the calendar layout for a convenient listing of course times and room requests.
  • Click here for a printable version of this table only.

All undergraduate courses except EN.500.112 will initially be listed as CS/CE majors/minors only, plus some affiliated minors for certain courses. All graduate courses will initially be listed as CS & affiliated MSE programs only (differs by course). After the initial registration period for each group, these restrictions will be lifted - on or about Nov 15th. Please be considerate of our faculty time and do not email them seeking permission to bypass these restrictions.

New Area Designators - CS course area designators are changed effective July 2019. Previously there were 3 designations - Analysis, Systems, Applications - and these still appear in the course descriptions below for grandfathering purposes. Going forward there are 5 areas and many courses have been reclassified. These areas will be implemented as POS (program of study) tags in SIS and are listed below each course number in the listings table. There are also 2 extra tags for undergraduates. Here are the new areas and tags:

  • CSCI-APPL Applications
  • CSCI-RSNG Reasoning
  • CSCI-SOFT Software
  • CSCI-SYST Systems
  • CSCI-THRY Theory
  • CSCI-TEAM Team (undergraduate only)
  • CSCI-ETHS Ethics (undergraduate only)

Course Numbering Note - In order to be compliant with undergraduate students only in courses <=5xx and graduate students in courses >=6xx, we completely renumbered all the courses in the department in Fall 2017, with a 601 prefix instead of the old 600 prefix. Courses are listed here with new numbers only - note that some suffixes were changed as noted in bold. Grad students must take courses 601.6xx and above to count towards their degrees. Combined bachelors/masters students may count courses numbered 601.4xx towards their masters degree if taken before the undergrad degree was completed. [All co-listed 601.4xx/6xx courses are equivalent.]

Courses without end times are presumed to meet for 50 minute periods. Final room assignments will be available on the Registrar's website in January. Changes to the original SIS-posted schedule are noted in red.



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.

See SIS class search for sections.

MWF 50 minutes, limit 19/section

601.104 (H)
CSCI-ETHS

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 4:30-6:30, alternate weeks (start 1/29)
Sec 02: Wed 4:30-6:30, alternate weeks (start 2/5)
Sec 03: Tue 4:30-6:30, alternate weeks (start 1/28)
Sec 04: Tue 4:30-6:30, alternate weeks (start 2/4)
limit 25, CS majors only

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. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages.

Prereq: C+ or better grade in AP CS, 601.107, 600.112, 500.112/113/114, 580.200 or equivalent.

Sec 01 (Hovemeyer): MWF 12-1:15
Sec 02 (Hovemeyer): MWF 1:30-2:45
Sec 03 (Darvish): MWF 3-4:15
Sec 04 (Darvish): MWF 4:30-5:45
Sec 05 (Sing Chun Lee): MWF 8:30-9:45 (EE/CE students only)
limit 34/section

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: C+ or better grade in 500.112, 601.107, or 601.220.

Sec 01: MWF 12-1:15
Sec 02: MWF 1:30-2:45
limit 75/section

601.229 (E)

COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer/Jin

We study the design and performance of a variety of computer systems from simple 8-bit micro-controllers through 32/64-bit RISC architectures all the way to ubiquitous x86 CISC architecture. We'll start from logic gates and digital circuits before delving into arithmetic and logic units, registers, caches, memory, stacks and procedure calls, pipelined execution, super-scalar architectures, memory management units, etc. Along the way we'll study several typical instruction set architectures and review concepts such as interrupts, hardware and software exceptions, serial and other peripheral communications protocols, etc. A number of programming projects, frequently done in assembly language and using various processor simulators, round out the course.

Prereq: 601.220.

Sec 01: MWF 10

Sec 02: MW 3-4:15
limit 75/section

601.231 (EQ)

AUTOMATA and 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.

TuTh 1:30-2:45
limit 125

601.290 (E)
CSCI-TEAM

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.

TuTh 3-4:15
limit 48

601.295 (E)
CSCI-TEAM

CANCELED

DEVELOPING HEALTH IT APPLICATIONS (3) Overby & Zach-Doughty

This course is a project-based introduction to working on successful projects in health care. In the first half of the term, students perform reading and homework assignments designed to introduce: (1) the context of health care delivery and health IT, (2) techniques to overcome challenges to conducting health care data analyses, and (3) techniques to design meaningful applications around health care data. In the second half of the term, students work in small groups to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building. [Oral starting 2019]

Prereq: 601.220 and 601.226.

CANCELED
(was TuTh 9-10:15)
limit 35

601.315 (E)
CSCI-SOFT

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] (www.cs.jhu.edu/~yarowsky/cs415.html)

Prereq: 600/601.226. Students may receive credit for only one of 601.315/415/615.

TuTh 9-10:15
limit 90

601.320 (E)
CSCI-SOFT

PARALLEL PROGRAMMING (3) Burns

This course prepares the programmer to tackle the massive data sets and huge problem size of modern scientific and enterprise computing. Google and IBM have commented that undergraduate CS majors are unable to "break the single server mindset" (http://www.google.com/intl/en/ press/pressrel/20071008_ibm_univ.html). Students taking this course will abandon the comfort of serial algorithmic thinking and learn to harness the power of cutting-edge software and hardware technologies. The issue of parallelism spans many architectural levels. Even ``single server'' systems must parallelize computation in order to exploit the inherent parallelism of recent multi-core processors. The course will examine different forms of parallelism in four sections. These are: (1) massive data-parallel computations with Hadoop!; (2) programming compute clusters with MPI; (3) thread-level parallelism in Java; and, (4) GPGPU parallel programming with NVIDIA's Cuda. Each section will be approximately 3 weeks and each section will involve a programming project. The course is also suitable for undergraduate and graduate students from other science and engineering disciplines that have prior programming experience. [Systems]

Prereq: 601.226 and 601.229. Students may receive credit for at most one of 601.320/420/620.

MW 4:30-5:45
limit 20

601.350 (E)
CSCI-APPL

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.

TuTh 3-4:15
limit 30

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
limit 5

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: Feb 3 - Mar 4

MW 4:30-5:45
5 weeks: 2/3-3/4
limit 30

601.411 (E)
CSCI-TEAM

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.

Th 4:30-7p
limit 20

601.414 (E)
CSCI-SYST

COMPUTER NETWORKS (3) Rubin/Kaptchuk

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.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614.

Sec 01 (Rubin): TuTh 9-10:15
Sec 02 (Kaptchuk): TuTh 1:30-2:45
limit 25/section

601.419 (E)
CSCI-SYST

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 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming.

MW 12-1:15
Sec 01: limit 20, CS/CE majors/minors
Sec 02: Canceled (limit 5, others by permission)

601.420 (E)
CSCI-SOFT

PARALLEL PROGRAMMING (3) Burns

More advanced version of 601.320. Students may receive credit for at most one of 601.320/420/620. [Systems]

Required course background: 601.226 and 601.229 or equiv.

MW 4:30-5:45
Section 01: limit 20

601.421 (E)
CSCI-SOFT, CSCI-TEAM

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] (https://www.jhu-oose.com)

Prereq: 600/601.226 & 600.120/601.220. Students may receive credit for only one of 601.421/621.

Lec: Tu 12-1:15
Sec 01: Th 12-1:15
Sec 02: Th 12-1:15
Sec 03: Th 12-1:15
Sec 04: Th 12-1:15
limit 20/section

601.426 (EQ)
CSCI-THRY

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
limit 30

601.433 (EQ)
CSCI-THRY

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 Perm. Req'd. Students may receive credit for only one of 601.433/633.

TuTh 12-1:15
limit 60

601.436 (EQ)
CSCI-THRY

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. Pre-req: 601.433/633 or permission. [Analysis]

Prereq: 600.433/633 or permission.

TuTh 3-4:15
limit 30

601.445 (E)
CSCI-SOFT

PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green

[Co-listed with 650.445.] 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. [Systems]

Prereq: EN.600/601.226 and EN.600.233/601.229. Students may receive credit for only one of 601.445/645.

MW 12-1:15
limit 25

601.446 (EN)
CSCI-THRY
NEW COURSE!

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. The course will involve significant programming projects. [Analysis]

Pre-req: 600.120/601.220, 600/601.226. Students may receive credit for 601.446 or 601.646, but not both.

TuTh 12-1:15
limit 15

601.454 (E) CSCI-APPL

AUGMENTED REALITY (3) Navab

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.

TuTh 9-10:15
starts at 8:30am weeks 1-6
limit 20

601.456 (E)
CSCI-APPL

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 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 601.456 or 601.656, but not both.
Note: Grad students taking this course should register for 600.656 instead.

TuTh 1:30-2:45
limit 18

601.496 (E)
CSCI-APPL, CSCI-TEAM

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
limit 12

601.459 (EQ) CSCI-THRY

COMPUTATIONAL GEOMETRY Kazhdan

This course will provide an introduction to computational geometry. It will cover a number of topics in two- and three-dimensions, including polygon triangulations and partitions, convex hulls, Delaunay and Voronoi diagrams, arrangements, and spatial queries. Time-permitting, we will also look at kD-trees, general BSP-trees, and quadtrees. [Analysis]

Pre-req: 601.220, 601.226, 601.433/633. Students may receive credit for 601.459 or 601.659, but not both.

MW 1:30-2:45
limit 20

601.461 (EQ)
CSCI-APPL

COMPUTER VISION (3) Shen

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. [Applications] (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.

TuTh 9-10:15
limit 30

601.463 (E)
CSCI-APPL

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, probability. Students may receive credit for only one of 601.463/663.

TuTh 12-1:15
limit 30

601.464 (E)
CSCI-RSNG

ARTIFICIAL INTELLIGENCE (3) Koehn/Poliak

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

Sec 01 (Koehn): TuTh 1:30-2:45, limit 40
Sec 02 (Poliak): MW 3-4:15, limit 30

601.466 (E)
CSCI-APPL
ADDED!

INFORMATION RETRIEVAL & WEB AGENTS (3) staff

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 12-1:15
limit 30

601.475 (E)
CSCI-RSNG

MACHINE LEARNING (3) Graff

Machine learning is subfield of computer science and artificial intelligence, whose goal is to develop computational systems, methods, and algorithms that can learn from data to improve their performance. This course introduces the foundational concepts of modern Machine Learning, including core principles, popular algorithms and modeling platforms. This will include both supervised learning, which includes popular algorithms like SVMs, logistic regression, boosting and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include a heavy programming components, requiring students to implement several machine learning algorithms in a common learning framework. Additionally, analytical homework questions will explore various machine learning concepts, building on the pre-requisites that include probability, linear algebra, multi-variate calculus and basic optimization. Students in the course will develop a learning system for a final project. [Applications or Analysis] Students may receive credit for only one of 601.475/675.

Required course background: multivariable calculus, probability, linear algebra.

MWF 4:30-5:45
limit 45

601.482 (E)
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING (4) staff

Deep learning (DL) has emerged as a powerful tool for solving data-intensive learning problems such as supervised learning for classification or regression, dimensionality reduction, and control. As such, it has a broad range of applications including speech and text understanding, computer vision, medical imaging, and perception-based robotics.
The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics.
Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. [Applications]

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.

MWF 8:30-9:45a
limit 40

601.486 (E)
CSCI-SOFT
NEW COURSE!

MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT (3) Unberath

The field of artificial intelligence (AI) has recently seen a substantial increase in popularity, largely fueled by the successes of training deep neural networks that achieve state-of-the-art performance in a large variety of problems. These successes are not limited to academic benchmarks but have started to impact our everyday lives in the form of products such as Google Lens, Amazon Alexa, and Tesla Autopilot. In order for such AI systems to succeed we must consider its impact on everyday life, its overall capabilities and performance, and the effectiveness of the human-AI interaction. The importance of harmonic interplay between all these components is dramatically highlighted by recent catastrophic events in road transport and aviation. In this project-based course you will work in teams of 3-5 students to 1) Identify a need with high-impact implications on everyday life; 2) Conceptualize and design an AI system targeting this need, and 3) Develop the AI system by refining a demo-able prototype based on feedback received during course presentations.

Pre-req: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design).

TuTh 1:30-2:45
limit 15

EN.580.488 (E)
CSCI-APPL

FOUNDATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS II Karchin

[BME cross-list, counts as CS upper level credit.] This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Foundations of Computational Biology I is not a prereq. [Analysis]

Required course background: math through linear algebra and differential equations, at least one statistics and probability course, 580.221 or equiv., 601.226 or equiv.

MW 4:30-5:45
Lab W 5:50-6:45

601.491 (E)
CSCI-APPL

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
limit 10

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 508 or 518.

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.

Th 4:30-7p
limit 5

601.614
CSCI-SYST

COMPUTER NETWORKS Rubin/Kaptchuk

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
Sec 02 (Kaptchuk): TuTh 1:30-2:45
limit 25/section

601.619
CSCI-SYST

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]

Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming.

MW 12-1:15
Sec 01: limit 20, CS students
Sec 02: Canceled (limit 5, others by permission)

601.620
CSCI-SOFT

PARALLEL PROGRAMMING (3) Burns

Same as 601.420, for graduate students. Students may receive credit for at most one of 601.320/420/620. [Systems]

Required course background: 601.226 and 601.229 or equiv.

MW 4:30-5:45
Section 01: limit 25

601.621
CSCI-SOFT

OBJECT ORIENTED SOFTWARE ENGINEERING Madooei

Same material as 601.421, for graduate students. [Systems or Applications] (https://www.jhu-oose.com)

Required course background: Intermediate Programming & Data Structures. Students may receive credit for only one of 601.421/621.

Lec: Tu 12-1:15
Sec 01: Th 12-1:15
Sec 02: Th 12-1:15
limit 20/section

601.626
CSCI-THRY

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
limit 30

601.633
CSCI-THRY

INTRO ALGORITHMS Braverman

Same as 601.433, for graduate students. [Analysis]

Prereq: 601.226 and 553.171/172 or Perm. Req'd. Students may receive credit for only one of 601.433/633.

TuTh 12-1:15
limit 40

601.636
CSCI-THRY

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: 600.433/633 or permission.

TuTh 3-4:15
limit 30

601.645
CSCI-SOFT

PRACTICAL CRYPTOGRAPHIC SYSTEMS Green

[Co-listed with 650.445.] Same material as 601.445, for graduate students. [Systems]

Prereqs? Students may receive credit for only one of 601.445/645.

MW 12-1:15
limit 30

601.646
CSCI-THRY
NEW COURSE!

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. The course will involve significant programming projects. [Analysis]

Pre-req: Intermediate Programming & Data Structures. Students may receive credit for 601.446 or 601.646, but not both.

TuTh 12-1:15
limit 15

601.654
CSCI-APPL

AUGMENTED REALITY (3) Navab

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.

TuTh 9-10:15
starts at 8:30am weeks 1-6
limit 20

601.656
CSCI-APPL

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
limit 35

601.659
CSCI-THRY

COMPUTATIONAL GEOMETRY Kazhdan

This course will provide an introduction to computational geometry. It will cover a number of topics in two- and three-dimensions, including polygon triangulations and partitions, convex hulls, Delaunay and Voronoi diagrams, arrangements, and spatial queries. Time-permitting, we will also look at kD-trees, general BSP-trees, and quadtrees. [Analysis]

Pre-req: 601.220, 601.226, 601.433/633. Students may receive credit for 601.459 or 601.659, but not both.

MW 1:30-2:45
limit 20

601.661
CSCI-APPL

COMPUTER VISION Shen

Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus)

Required course background: intro programming & linear algebra & prob/stat

TuTh 9-10:15
limit 30

601.663
CSCI-APPL

ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard

Same as 601.463, for graduate students. [Analysis]

Required course background: 601.226, calculus, prob/stat. Students may receive credit for only one of 601.463/663.

TuTh 12-1:15
limit 40

601.664
CSCI-RSNG

ARTIFICIAL INTELLIGENCE Koehn

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 1:30-2:45
limit 40

601.666
CSCI-APPL
ADDED!

INFORMATION RETRIEVAL & WEB AGENTS (3) staff

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 12-1:15
limit 30

601.675
CSCI-RSNG

MACHINE LEARNING Graff

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 4:30-5:45
limit 45

601.682
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING staff

Same as 601.482, for graduate students. [Applications]

Required course background: data structures, probability and linear algebra; numerical optimization and Python recommended.

MWF 8:30-9:45a
limit 40

601.686
CSCI-SOFT
NEW COURSE!

MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT Unberath

The field of artificial intelligence (AI) has recently seen a substantial increase in popularity, largely fueled by the successes of training deep neural networks that achieve state-of-the-art performance in a large variety of problems. These successes are not limited to academic benchmarks but have started to impact our everyday lives in the form of products such as Google Lens, Amazon Alexa, and Tesla Autopilot. In order for such AI systems to succeed we must consider its impact on everyday life, its overall capabilities and performance, and the effectiveness of the human-AI interaction. The importance of harmonic interplay between all these components is dramatically highlighted by recent catastrophic events in road transport and aviation. In this project-based course you will work in teams of 3-5 students to 1) Identify a need with high-impact implications on everyday life; 2) Conceptualize and design an AI system targeting this need, and 3) Develop the AI system by refining a demo-able prototype based on feedback received during course presentations.

Required course background: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design).

TuTh 1:30-2:45
limit 15

580.688
CSCI-APPL

FOUNDATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS II Karchin

[Cross-listed from BME - CS students can count as a CS course.] This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Foundations of Computational Biology I is not a prereq. [Analysis]

Required course background: math through linear algebra and differential equations, at least one statistics and probability course, 580.221 or equiv., 601.226 or equiv.

MW 4:30-5:45
Lab W 5:50-6:45

601.691
CSCI-APPL

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
limit 20

601.717
CSCI-SYST

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
limit 15

601.718
CSCI-SYST

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.

TuTh 1:30-2:45
limit 20

601.742
CSCI-THRY

ADVANCED TOPICS IN CRYPTOGRAPHY (3) Jain

[Cross-listed in ISI] This course will focus on advanced cryptographic topics with an emphasis on open research problems and student presentations. [Analysis]

Prereq: 600.442 or 600.472 or permission.

F 1:30-4
limit 15

580.743
CSCI-APPL

ADV TOPICS IN GENOME DATA ANALYSIS Battle

[Formerly 600.641/601.751] Genomic data is becoming available in large quantities, but understanding how genetics contributes to human disease and other traits remains a major challenge. Machine learning and statistical approaches allow us to automatically analyze and combine genomic data, build predictive models, and identify genetic elements important to disease and cellular processes. This course will cover current uses of statistical methods and machine learning in diverse genomic applications including new genomic technologies. Students will present and discuss current literature. Topics include personal genomics, integrating diverse genomic data types, new technologies such as single cell sequencing and CRISPR, and other topics guided by student interest. The course will include a project component with the opportunity to explore publicly available genomic data.

Recommended Course Background: coursework in data science or machine learning.

MW 3-4:15
limit 20

601.749
CSCI-APPL

COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz

The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm & final exam, class presentations, and a significant class project. [Applications]

Prereq: familiarity with UNIX scripting and/or programming.

MW 1:30-2:45
limit 30

601.767
CSCI-APPL

NEW COURSE!

DEEP LEARNING FOR AUTOMATED DISCOURSE Sedoc

The overall objective of this course is for students to learn about state-of-the-art research in dialog systems, particularly focused on deep learning methods. Students will also learn how to read and navigate academic literature. The class will be centered around a 2-3 person project, with presentations/demos at the end of the semester. Students are expected to read, write, and review workshop-level academic papers. [Applications]

Required course background: EN.601.467/667 or [(EN.601.475/675 or EN.601.482/682) and (EN.601.465/665 or EN.601.468/668 or EN.601.765)] or permission.

TuTh 3-4:15
limit 25

601.783
CSCI-APPL

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
limit 30

601.801

COMPUTER SCIENCE SEMINAR

Required for all CS PhD students. Strongly recommended for MSE students.

TuTh 10:30-12
limit 90

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

601.808

SELECTED TOPICS IN CS EDUCATION Selinski/More

This course will explore current issues and research in computer science education. Topics will be drawn from literature, news items, and participant experience. Current faculty and students wtih interests in academic careers are encouraged to attend. Permission required.

limit 15

601.809

PHD RESEARCH

See below for faculty section numbers.

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
limit 14

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
limit 30

601.856

SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince

[Co-listed as 520.746] 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 3-4:50

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
limit 8

601.865

SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Sedoc

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
limit 15

601.866

SELECTED TOPICS IN MEANING, TRANSLATION AND GENERATION OF TEXT VanDurme

A seminar focussed on current research and survey articles on computational semantics.

Fr 10-10:50
limit 15

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.

T 9:30-10:30
limit 15

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
limit 80

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

530.707

ROBOT SYSTEM PROGRAMMING Whitcomb

(see SIS)

TuTh 4:30-5:45
permission only

Faculty section numbers for all independent type courses, undergraduate and graduate.

01 - Xin Li
02 - Rao Kosaraju
03 - Soudeh Ghorbani
04 - Russ Taylor (ugrad research use 517, not 507)
05 - Scott Smith
06 - Joanne Selinski
07 - Harold Lehmann
08 - Joao Sedoc
09 - Greg Hager (ugrad research use 517, not 507)
10 - Gregory Chirikjian
11 - Sanjeev Khudhanpur
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
20 - Michael Schatz
21 - Avi Rubin
22 - Matt Green
23 - Yinzhi Cao
24 - Raman Arora (ugrad research use 517, not 507)
25 - Rai Winslow
26 - Misha Kazhdan
27 - Chris Callison-Burch
28 - Ali Darvish
29 - Alex Szalay
30 - Peter Kazanzides
31 - Jerry Prince
32 - Carey Priebe
33 - Nassir Navab
34 - Rene Vidal
35 - Alexis Battle (ugrad research use 517, not 507)
36 - Emad Boctor (ugrad research use 517, not 507)
37 - Mathias Unberath
38 - Ben VanDurme
39 - Jeff Siewerdsen
40 - Vladimir Braverman
41 - Suchi Saria
42 - Ben Langmead
43 - Steven Salzberg
44 - Tal Linzen
45 - Liliana Florea
46 - Casey Overby Taylor
47 - Philipp Koehn
48 - Abhishek Jain
49 - Anton Dahbura (ugrad research use 517, not 507)
50 - Joshua Vogelstein
51 - Ilya Shpitser
52 - Austin Reiter
53 - Tamas Budavari
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
60 - Mihaela Pertea [staff]
61 - Archana Venkataraman