Fall 2019 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. The calendar layout uses the NEW course numbers.
  • 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. Current restriction expiration dates are 4/15/19 for most undergraduate courses and 8/15/2019 for most graduate courses (after incoming graduate students have had a chance to register). 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 assumed to meet for 50 minute periods. Final room assignments will be available on SIS and the Registrar's website in September. Changes to the original SIS schedule will be 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.

Sections start on the hour, from 9a - 4p. Sections 6 & 7 are restricted to incoming CS majors.

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 4:30-6:30p, alternate weeks starting 9/4
Sec 02: Wed 4:30-6:30p, alternate weeks starting 9/11
limit 19 each, CS majors only

601.220 (E)

INTERMEDIATE PROGRAMMING (4) Hovemeyer/Darvish

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: AP CS or >=C+ grade in one of 601/600.107, 500.112, 500.113, 500.114, 580.200) or equivalent by permission.

CS/CE majors/minors only
Sec 01: MWF 12:00-1:15
Sec 02: MWF 1:30-2:45 (incoming CS freshmen only)
Sec 03: MWF 3:00-4:15
Sec 04: MWF 4:30-5:45 (was MWF 8:30-9:45)
limit 35/section

601.226 (EQ)

DATA STRUCTURES (4) Selinski/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: AP CS or >= C+ grade in 600.107/601.107, 600.120/601.220, 500.112, 500.113+500.132, 500.114+500.132 or equivalent by permission.

Sec 01: MWF 1:30-2:45, limit 75
Sec 02: MWF 4:30-5:45, limit 45

601.229 (E)

COMPUTER SYSTEM FUNDAMENTALS (3) Koehn/Hovemeyer

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, superscalar 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: 600.120/601.220.

01: MWF 10, limit 90
02: MWF 10, limit 45

601.231 (EQ)

AUTOMATA and COMPUTATION THEORY (3) Kosaraju

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 601.231/600.271 and 601.631/600.471 for the same degree.

Prereq: 550/553.171/172.

01: TuTh 9-10:15, limit 75
02: TuTh 1:30-2:45, limit 45

601.315 (E)
CSCI-SOFT

DATABASES (3) Yarowsky

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 3-4:15
limit 30

601.318 (E)
CSCI-SYST

OPERATING SYSTEMS (3) Huang

This course covers the fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. [Systems]

Prereq: 600.120/601.220 & 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45
limit 20

601.340 (E)
CSCI-SOFT

WEB SECURITY (3) Cao

This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]

Note: This undergrad version will not have the same paper component as the other versions of this course. Prerequisite: 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.340/440/640.

TuTh 12-1:15
limit 15

601.382 (E)

DEEP LEARNING LAB (1) Unberath

This course is an optional hands-on lab supplement for a few courses in the curriculum. It will provide tutorial support and practical experience for developing deep ML systems using PyTorch and TensorFlow, and may provide exposure to some other frameworks. It will also go into detail on practical methods for scalable learning on large data sets, and other more practical issues in setting up deep learning systems.

Co-req: EN.601.482 or EN.601.682 or EN.601.765.

Tu 4:30-6:30
S/U only
limit 38

601.415 (E)
CSCI-SOFT

DATABASES (3) Yarowsky

Similar material as 601.315, covered in more depth, for advanced undergraduates. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html)

Prereq: 600.226/601.226. Students may receive credit for only one of 601.315/415/615.

TuTh 3-4:15
limit 18

601.417 (E)
CSCI-SYST

DISTRIBUTED SYSTEMS (3) Amir

The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive hands-on experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester (odd years) and is a good introduction course to the 601.717 Advanced Distributed Systems and Networks project-focused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses)

Prereq: 600.120/601.220 & 600/601.226. Students may receive credit for only one of 601.317/417/617.

TuTh 3-4:15
limit 20

601.418 (E)
CSCI-SYST

OPERATING SYSTEMS (3) Huang

Similar material as 600.318, covered in more depth, for advanced undergraduates. [Systems]

Prereq: 600.120/601.220 & 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45
limit 20

601.421 (E)
CSCI-SOFT, CSCI-TEAM

OBJECT ORIENTED SOFTWARE ENGINEERING (3) Facchinetti

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.

MW 1:30-2:45
limit 75

601.422 (E)
CSCI-SOFT
NEW COURSE!

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
limit 25

601.427 (E)
CSCI-THRY
NEW COURSE!

PRINCIPLES OF PROGRAMMING LANGUAGES II (3) Smith

This course is designed as a follow-on to Principles of Programming languages. It will cover a wide array of fundamental topics in programming languages, including advanced functional programming, the theory of inductive definitions, advanced operational semantics, advanced type systems, program analysis, program verification, theorem provers and SAT solvers. [Analysis]

Pre-req: 601.426 or instructor permission. Students may receive credit for 601.427 or 601.627, but not both.

MWF 10
limit 8

601.430 (EQ)
CSCI-THRY

COMBINATORICS AND GRAPH THEORY IN CS Li

This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis]

Pre-requisite: 550.171/553.171/553.172; probability theory and linear algebra recommended. Students may receive credit for only one of EN.601.430 and EN.601.630.

TuTh 12-1:15
limit 10

601.433 (EQ)
CSCI-THRY

INTRO ALGORITHMS (3) Dinitz

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: 601/600.226 & (550/553.171/172 or 601.231/600.271) or Perm. Req'd. Students may receive credit for only one of 601.433/633.

TuTh 12-1:15
limit 75

601.434 (EQ)
CSCI-THRY

RANDOMIZED & BIG DATA ALGORITHMS (3) Braverman

The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered includee: tail inequalities, linear programming relaxation & randomized rounding, de-randomization, existence proofs, universal hashing, markov chains, metropolis and metropolis-hastings methods, mixing by coupling and by eigenvalues, counting problems, semi-definite programming and rounding, lower bound arguments, and applications of expanders. [Analysis] (www.cs.jhu.edu/~cs464)

Prereq: 600.363/463/601.433/633 and (550.310/553.310/553.311 or 550.420/620 or equivalent). Students may receive credit for only one of 601.434/634.

TuTh 12-1:15
limit 25

601.440 (E)
CSCI-SOFT

WEB SECURITY (3) Cao

This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]

Prerequisite: 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.340/440/640.

TuTh 12-1:15
limit 10

601.442 (EQ)
CSCI-THRY

MODERN CRYPTOGRAPHY (3) Jain

Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zero-knowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. [Analysis]

Prerequisite: 601.231/600.271/471 & (550.310/553.310 or 553.331 or 550.420/553.420). Students may receive credit for only one of 601.442/642.

MW 1:30-2:45
limit 25

601.443 (E)
CSCI-SOFT

SECURITY AND PRIVACY IN COMPUTING (3) Rubin

Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. [Applications]

Prerequisite: 600.233/601.229 & (600/601.318/418 or 600.444/601.414) Students may receive credit for only one of 601.443/643.

TuTh 9-10:15
limit 30

601.444 (E)
CSCI-SOFT
REPLACING 650.724

NETWORK SECURITY (3) Nielson

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. [Systems]

Prerequisites: 600.120/601.220, 600/601.226, 600.344/444/601.314/414/614 or permission. Students can receive credit for only one of 601.444/644.

MW 3-4:15
limit 5

601.447 (E)
CSCI-APPL, CSCI-TEAM

COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead/Solomon

Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming nprojects. [Applications, Oral]

Prereq: 600.120/601.220 & 600/601.226. Students may receive credit for at most one of 601.447/647/747.

TuTh 12-1:15
Sec 01: limit 25
Sec 02: limit 19

601.452 (E)
(no tag)

COMPUTATIONAL BIOMEDICAL RESEARCH (3) Schatz

[Co-listed with AS.020.415] This course for advanced undergraduates includes classroom instruction in interdisciplinary research approaches and lab work on an independent research project in the lab of a Bloomberg Distinguished Professor and other distinguished faculty. Lectures will focus on cross-cutting techniques such as data visualization, statistical inference, and scientific computing. In addition to two 50-minute classes per week, students will commit to working approximately 3 hours per week in the lab of one of the professors. The student and professor will work together to schedule the research project. Students will present their work at a symposium at the end of the semester. Prereq: permission required.

MW 3-3:50
limit 15

601.455 (E)
CSCI-APPL

COMPUTER INTEGRATED SURGERY I (4) Taylor

This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. [Applications] (http://www.cisst.org/~cista/445/index.html)

Prereq: 600/601.226 and linear algebra, or permission. Recmd: 600.120/601.220, 600/601.457, 600/601.461, image processing. Students may earn credit for only one of 601.455/655.

TuTh 1:30-2:45
limit 40

601.457 (EQ)
CSCI-APPL

COMPUTER GRAPHICS (3) Kazhdan

This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. [Applications]

Prereq: no audits; 600.120/601.220 & 600/601.226 & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.

MWF 11
limit 50

601.461 (EQ)
CSCI-APPL

COMPUTER VISION (3) Hager

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 50

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: 600.226/601.226 & linear algebra & probability. Students may receive credit for only one of 600.336/436/636 and 601.463/663/763.

TuTh 4:30-5:45
limit 35

601.464 (E)
CSCI-RSNG

ARTIFICIAL INTELLIGENCE (3) VanDurme

The class is recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as learning, planning and prediction. Materials will be primarily based on the popular textbook, Artificial Intelligence: A Modern Approach. Strong programming skills are expected, as well as basic familiarity with probability. For students intending to also take courses in Machine Learning (e.g., 601.475/675, 601.476/676), they may find it beneficial to take this course first, or concurrently. [Applications]

Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664

TuTh 9-10:15
limit 45

601.465 (E)
CSCI-APPL
REINSTATED

NATURAL LANGUAGE PROCESSING (3) Duh

This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers? The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition and machine translation. There are a number of structured but challenging programming assignments. [Applications] (www.cs.jhu.edu/~jason/465)

Prerequisite: 600/601.226. Students may receive credit for at most one of 601.465/665.

MWF 11
limit 35

601.467 (E)
CSCI-APPL
NEW COURSE

INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn

This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. [Applications]

Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667.

MW 3-4:15
limit 30

601.475 (E)
CSCI-RSNG

MACHINE LEARNING (3) Dredze

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]

Pre-reqs: multivariable calculus (110.202 or 110.211) & probability (550.310/553.310/553.311 or 550.420/553.420 or 560.348) & linear algebra (110.201 or 110.212 or 553.291) & intro computing (EN.500.112, EN.500.113, EN.500.114, EN.601.220/600.120, AS.250.205, EN.580.200, EN.600/601.107). Students may receive credit for only one of 601.475/675.

MW 1:30
limit 50

601.477 (EQ)
CSCI-RSNG

CAUSAL INFERENCE (3) Shpitser

"Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. [Analysis]

Pre-requisites: familiarity with R programming and (600/601.475/675 or stats/probability) or permission. Students may receive credit for at most one of 601.477/677.

TuTh 3-4:15
limit 20

601.482 (E)
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING (3) 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.
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]
Students should also consider taking EN.601.382 Deep Learning Lab as a supplement. Students may choose to skip the lab course if they already have a strong programming background and are comfortable learning on their own using online resources and tutorials.

Pre-req: (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) and (EN.601.475 or equiv); Calc III and numerical optimization recommended. Recommended co-req: EN.601.382.

TuTh 12-1:15
limit 40

AS.050.375 (Q)
CSCI-APPL

PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille

[Was EN.601.485, now cross-listed as AS.050.375] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. [Applications or Analysis]

Pre-requisites: Calc I, programming experience (Python preferred).

TuTh 9-10:15
limit 25

601.490 (E)
CSCI-SOFT, CSCI-TEAM

INTRO TO HUMAN-COMPUTER INTERACTION (3) C-M Huang

This course is designed to introduce undergraduate and graduate students to design techniques and practices in human-computer interaction (HCI), the study of interactions between humans and computing systems. Students will learn design techniques and evaluation methods, as well as current practices and exploratory approaches, in HCI through lectures, readings, and assignments. Students will practice various design techniques and evaluation methods through hands-on projects focusing on different computing technologies and application domains. This course is intended for undergraduate and graduate students in Computer Science/Cognitive Science/Psychology. Interested students from different disciplines should contact the instructor before enrolling in this course. [Applications]

Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both.

TuTh 3-4:15
Sec 01: CS majors/minors, limit 30
Sec 02: KSAS students, limit 10

601.501

COMPUTER SCIENCE WORKSHOP

[Formerly 600.491] An applications-oriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal.

Perm. of faculty supervisor req'd.

See below for faculty section numbers

601.503

INDEPENDENT STUDY

Individual, guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved.

Permission required.

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 and credit assigned 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. S/U only.

Permission 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.

601.519

SENIOR HONORS THESIS (3)

For computer science majors only. The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publically before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors.

Prereq: 3.5 GPA in Computer Science after spring of junior year and permission of faculty supervisor.

See below for faculty section numbers

601.556

SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3)

The student will undertake a substantial independent research project in the area of computer-integrated surgery, under joint supervision of a WSE faculty adviser and a clinician or clinical researcher at the Johns Hopkins Medical School.

Prereq: 600.445 or perm req'd.

Section 1: Taylor

601.615
CSCI-SOFT

DATABASES Yarowsky

Same material as 601.415, for graduate students. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html)

Required course background: Data Structures. Students may receive credit for only one of 601.315/415/615.

TuTh 3-4:15
limit 44

601.617
CSCI-SYST

DISTRIBUTED SYSTEMS (3) Amir

The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive hands-on experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester (odd years) and is a good introduction course to the 601.717 Advanced Distributed Systems and Networks project-focused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses)

Prereq: Intermediate Programming (C/C++) and Data Structures. Students may receive credit for only one of 601.317/417/617.

TuTh 3-4:15
limit 20

601.618
CSCI-SYST

OPERATING SYSTEMS Huang

Same material as 601.418, for graduate students. [Systems]

Required course background: Data Structures & Computer System Fundamentals. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45
limit 20

601.621
CSCI-SOFT

OBJECT ORIENTED SOFTWARE ENGINEERING Facchinetti

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.

MW 1:30-2:45
limit 40 [+ 10 incoming]

601.622
CSCI-SOFT
NEW COURSE!

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
limit 13

601.627
CSCI-THRY
NEW COURSE!

PRINCIPLES OF PROGRAMMING LANGUAGES II (3) Smith

This course is designed as a follow-on to Principles of Programming languages. It will cover a wide array of fundamental topics in programming languages, including advanced functional programming, the theory of inductive definitions, advanced operational semantics, advanced type systems, program analysis, program verification, theorem provers and SAT solvers. [Analysis]

Pre-req: 601.426 or instructor permission. Students may receive credit for 601.427 or 601.627, but not both.

MWF 10
limit 10

601.630
CSCI-THRY

COMBINATORICS AND GRAPH THEORY IN CS Li

This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis]

Required Background: discrete math; probability theory and linear algebra recommended. Student may receive credit for only one of 601.430/601.630.

TuTh 12-1:15
limit 20

601.633
CSCI-THRY

INTRO ALGORITHMS Dinitz

Same material as 600.433, for graduate students. [Analysis]

Required Background: Data Structures and (Discrete Math or Automata/Computation Theory). Students may receive credit for only one of 601.433/633.

TuTh 12-1:15
limit 65

601.634
CSCI-THRY

RANDOMIZED & BIG DATA ALGORITHMS Braverman

Same material as 601.434, for graduate students. [Analysis] (www.cs.jhu.edu/~cs464)

Required Background: Algorithms and probability. Students may receive credit for only one of 601.434/634.

TuTh 12-1:15
limit 25

601.640
CSCI-SOFT

WEB SECURITY (3) Cao

This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]

Required course background: data structures and computer system fundamentals. Students may receive credit for only one of 601.340/440/640.

TuTh 12-1:15
limit 25

601.642
CSCI-THRY

MODERN CRYPTOGRAPHYJain

Same material as 601.442, for graduate students. [Analysis]

Required course background: Probability & Automata/Computation Theory.

MW 1:30-2:45
limit 25

601.643
CSCI-SOFT

SECURITY AND PRIVACY IN COMPUTING Rubin

Same material as 601.443, for graduate students. [Applications]

Required course background: A basic course in operating systems and networking, or permission of instructor.

TuTh 9-10:15
limit 40

601.644
CSCI-SOFT
REPLACING 650.724

NETWORK SECURITY Nielson

[Cross-listed in ISI] Same material as 601.444, for graduate students. [Systems]

Required course background: 600.120, 600.226, Computer Networks or permission. Students may receive credit for only one of 601.444/644.

MW 3-4:15
limit 35

601.647
CSCI-APPL

COMPUTATIONAL GENOMICS: SEQUENCES Langmead/Solomon

Same material as 601.447, for graduate students. [Applications]

Required Course Background: Intermediate Programming (C/C++) and Data Structures. Students may earn credit for at most one of 601.447/647/747.

TuTh 12-1:15
Sec 01: limit 14
Sec 02: limit 25

601.655
CSCI-APPL

COMPUTER INTEGRATED SURGERY I Taylor

Same material as 601.455, for graduate students. [Applications] (http://www.cisst.org/~cista/445/index.html)

Prereq: data structures and linear algebra, or permission. Recommended: intermediate programming in C/C++, computer graphics, computer vision, image processing. Students may earn credit for 601.455 or 601.655, but not both.

TuTh 1:30-2:45
limit 30

601.657
CSCI-APPL

COMPUTER GRAPHICS Kazhdan

Same material as 601.457, for graduate students.

Prereq: no audits; Intermediate Programming (C/C++) & Data Structures & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.

MWF 11
limit 50

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 4:30-5:45 (was 9-10:15)
limit 55

601.663
CSCI-APPL

ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard

Same material as EN.601.463, for graduate students. [Analysis]

Required course background: data structures & linear algebra & prob/stat. Students may receive credit for only one of 600.336/436/636 or 601.463/663/763.

TuTh 4:30-5:45
limit 40

601.664 (600.435)
CSCI-RSNG

ARTIFICIAL INTELLIGENCE VanDurme

Same as 601.464, for graduate students. [Applications]

Prereq: Data Structures; Recommended: linear algebra & prob/stat. Students can only receive credit for one of 601.464/664

TuTh 9-10:15
limit 45

601.665
CSCI-APPL
REINSTATED

NATURAL LANGUAGE PROCESSING Duh

Same material as 601.465, for graduate students. [Applications] (www.cs.jhu.edu/~jason/465)

Prerequisite: data structures. Students may receive credit for at most one of 601.465/665.

MWF 11
limit 35

601.667 (E)
CSCI-APPL
NEW COURSE

INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn

This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. [Applications]

Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667.

MW 3-4:15
limit 30

601.675
CSCI-RSNG

MACHINE LEARNING Dredze

Same material as 601.475, for graduate students. [Applications or Analysis]

Required course background: multivariable calculus, probability, linear algebra, intro computing. Student may receive credit for only one of 601.475/675.

MW 1:30
limit 70

601.677
CSCI-RSNG

CAUSAL INFERENCE Shpitser

Same material as 601.477, for graduate students. [Analysis]

Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for at most one of 601.477/677.

TuTh 3-4:15
limit 40

601.682
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING Unberath

Same as 601.482, for graduate students. [Applications]
Students should also consider taking EN.601.382 Deep Learning Lab as a supplement. Students may choose to skip the lab course if they already have a strong programming background and are comfortable learning on their own using online resources and tutorials.

Required course background: probability and linear algebra, some machine learning; calc III and numerical optimization recommended. Recommended co-req: EN.601.382.

TuTh 12-1:15
limit 50

AS.050.675
CSCI-APPL

PROBABILISTIC MODELS OF THE VISUAL CORTEX Yuille

[Was EN.601.685, now cross-listed as AS.050.675.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. [Applications or Analysis]

Pre-requisites: Calc I, programming experience (Python preferred).

TuTh 9-10:15
limit 10

601.690
CSCI-SOFT

INTRO TO HUMAN-COMPUTER INTERACTION C-M Huang

Same material as EN.601.490, for graduate students. [Applications]

Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both.

TuTh 3-4:15
Sec 01: limit 20 (CS grad students or permission)

601.714
CSCI-SYST

ADVANCED COMPUTER NETWORKS Jin

This is a graduate-level course on computer networks. It provides a comprehensive overview on advanced topics in network protocols and networked systems. The course will cover both classic papers on Internet protocols and recent research results. It will examine a wide range of topics, e.g., routing, congestion control, network architectures, datacenter networks, network virtualization, software-defined networking, and programmable networks, with an emphasize on core networking concepts and principles. The course will include lectures, paper discussions, programming assignments and a research project. [Systems]
Recommended Course Background: One undergraduate course in computer networks (e.g., EN.601.414/614 Computer Network Fundamentals or the equivalent), or permission of the instructor. The course assignments and projects assume students to be comfortable with programming.

Pre-req: EN.601.414/614 or equivalent.

TuTh 1:30-2:45
limit 25

601.745
CSCI-THRY

ADVANCED TOPICS IN APPLIED CRYPTOGRAPHY (3) Green

[Cross-listed in ISI] This reading and project based course will explore the latest research in the area of applied cryptography and cryptographic engineering. Topics covered will include zero knowledge, efficient multiparty computation, cryptocurrencies, and trusted computing hardware. Readings will be drawn from the latest applied cryptography and security conferences. The course will include both reading, critical analysis, presentations and a course programming project. [Analysis or Applications]

Prereq: 600/650.454 or 601.445/645 (Practical Crypto) or 600/601.442/642 or permission.

MW 12-1:15
limit 25

580.745

MATHEMATICS OF DEEP LEARNING Vidal

[cross-listed from BME, 1.5 credits only] The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. For example, a key issue is that the training problem is nonconvex, hence optimization algorithms are not guaranteed to return a global minima. Another key issue is that while the size of deep networks is very large relative to the number of training examples, deep networks appear to generalize very well to unseen examples and new tasks. This course will overview recent work on the theory of deep learning that aims to understand the interplay between architecture design, regularization, generalization, and optimality properties of deep networks.

Fr 1-3p
limit 45

601.779
CSCI-RSNG

cancelled

MACHINE LEARNING: ADVANCED TOPICS (3) Arora

[Formerly called Advanced topics in Representation Learning]
This course will focus on recent advances in theory, methods and applications of representation learning. We will take a stochastic optimization view of representation learning, and explore various learning objectives and optimization algorithms for representation learning. In this stochastic optimization framework, we will present a unified view of different approaches to representation learning including function approximation, probabilistic, and information theoretic. We will study the optimization problems that result in each of these approaches and study algorithms for solving these optimization problems, both theoretically and empirically. Finally, we will discuss applications of these techniques to speech and language processing, computational healthcare, social media analytics, and computational neuroscience. [Analysis]

Pre-requisites: Representation Learning or permission (requiring all of the following):

  1. Linear algebra (vector spaces, orthogonality, basis, singular value decomposition)
  2. Probability and Statistics (random variables, probability distributions, expectation, mean, variance, covariance, conditional probability, Bayes rule)
  3. Introductory machine learning (classification, regression)
  4. Convex optimization (differentiation, chain rule, unconstrained optimization)
  5. Machine learning (differentiation, chain rule, unconstrained optimization)

MW 3-4:15
limit 30

601.801

COMPUTER SCIENCE SEMINAR

Attendance recommended for all grad students; only 1st & 2nd year PhD students may register.

TuTh 10:30-12
limit 90

601.803

MASTERS RESEARCH

Independent research for masters 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 Smith

PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement. Permission required.

limit 25

601.809

PHD RESEARCH

Independent research for PhD students.

See below for faculty section numbers.

AS.050.814

RESEARCH SEMINAR IN COMPUTER VISION Yuille

This course covers advanced topics in computational vision. It discusses and reviews recent progress and technical advances in visual topics such as object recognition, scene understanding, and image parsing.

tba

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 course covers recent developments in the foundations of programming language design and implementation. topics covered vary from year to year. Students will present papers orally.

Th 1-2
limit 15

601.831

CS THEORY SEMINAR Dinitz, Li

Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers.

W 12
limit 30

601.845

SELECTED TOPICS IN APPLIED CRYPTOGRAPHY Green

In this course students will read, discuss and present current research papers in applied cryptography. Topic coverage will vary each semester.

Prereq: permission of instructor.

Tu 12-12:50
limit 12

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.

W 1:30-2:45
limit 15

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.

Th 12
limit 15

601.866

SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme

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

Fr 10:45-11:45
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.

W 11-noon
limit 15

601.875

SELECTED TOPICS IN MACHINE LEARNING Arora

This seminar is recommended for all students interested in data intensive computing research areas (e.g., machine learning, computer vision, natural language processing, speech, computational social science). The meeting format is participatory. Papers that discuss best practices and the state-of-the-art across application areas of machine learning and data intensive computing will be read. Student volunteers lead individual meetings. Faculty and external speakers present from time-to-time.

Required course background: a machine learning course or permission of instructor.

Thu 3-4:15

520.702

CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING staff

CLSP seminar series, for any students interested in current topics in language and speech processing.

Tu & Fr 12-1:15

500.745

SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, 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

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

01 - Xin Li
02 - Rao Kosaraju
03 - Soudeh Ghorbani [Ahmad]
04 - Russ Taylor (ugrad research use 517, not 507)
05 - Scott Smith
06 - Joanne Selinski
07 - Harold Lehmann
08 - [John Sheppard]
09 - Greg Hager
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 [Terzis]
24 - Raman Arora (ugrad research use 517, not 507)
25 - Rai Winslow
26 - Misha Kazhdan
27 - Chris Callison-Burch
28 - Ali Darvish [Froehlich]
29 - Alex Szalay
30 - Peter Kazanzides
31 - Jerry Prince
32 - Carey Priebe [Rajesh Kumar]
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 [Joel Bader]
38 - Ben VanDurme
39 - Jeff Siewerdsen
40 - Vladimir Braverman
41 - Suchi Saria
42 - Ben Langmead
43 - Steven Salzberg
44 - Tal Linzen [Haider Ali]
45 - Liliana Florea
46 - Casey Overby Taylor [Adam Lopez]
47 - Philipp Koehn
48 - Abhishek Jain
49 - Anton Dabhura (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 - [Marin Kobilarov]