Fall 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. Current restriction expiration dates are 4/28/20 for most undergraduate courses and 8/1/2020 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 ??? 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 9/2
Sec 02: Wed 4:30-6:30p, alternate weeks starting 9/9
Sec 03: Tue 4:30-6:30p, alternate weeks starting 9/1
limit 19 each, CS majors only

601.220 (E)

INTERMEDIATE PROGRAMMING (4) Amir/Darvish/Selinski

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 (500.132 or 500.133 or 500.134) or equivalent by permission.

CS/CE majors/minors only
Sec 01 (Darvish): MWF 12:00-1:15
Sec 02 (Darvish): MWF 1:30-2:45
Sec 03 (Amir): MWF 3:00-4:15 (incoming CS freshmen only)
Sec 04 (Selinski): MWF 10-11:15am
limit 35/section

601.226 (EQ)

DATA STRUCTURES (4) Madooei/Schatz

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 (Madooei): MWF 12-1:15, limit 75
Sec 02 (Schatz): MWF 1:30-2:45, limit 75

601.229 (E)

COMPUTER SYSTEM FUNDAMENTALS (3) 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 9, limit 75
02: MWF 10, limit 75

601.231 (EQ)

AUTOMATA and COMPUTATION THEORY (3) More

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.

TuTh 9-10:15, limit 90

601.280 (E)
NEW COURSE!

FULL-STACK JAVASCRIPT (3) Madooei

A full-stack JavaScript developer is a person who can build modern software applications using primarily the JavaScript programming language. Creating a modern software application involves integrating many technologies - from creating the user interface to saving information in a database and everything else in between and beyond. A full-stack developer is not an expert in everything. Rather, they are someone who is familiar with various (software application) frameworks and the ability to take a concept and turn it into a finished product. This course will teach you programming in JavaScript and introduce you to several JavaScript frameworks that would enable you to build modern web, cross-platform desktop, and native/hybrid mobile applications. A student who successfully completes this course will be on the expedited path to becoming a full-stack JavaScript developer.

Prereq: 601.220 or 601.226. Students must not have taken or be concurrently enrolled in 601.421/621 OOSE.

01: TuTh 12-1:15p, limit 19
02: TuTh 1:30-2:45p, limit 19, rising soph/juniors only

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.

01: 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 15

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 25

601.329 (E)
CSCI-SOFT
NEW COURSE!

FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith

How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project. Pre-req: 601.226 or instructor permission.

MW 1:30-2:45
limit 19

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 10

601.414 (E)
CSCI-SYST

COMPUTER NETWORKS (3) Jin

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.

TuTh 1:30-2:45
limit 24

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

601.418 (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 15

601.419 (E)
CSCI-SYST

CLOUD COMPUTING (3) Ghorbani

Clouds host a wide range of applications that we rely on today. In this course, we study foundational cloud systems and infrastructures, common cloud applications and the traffic patterns that they generate, and core networking and distributed systems concepts and algorithms that enable cloud computing. We will also study how today's application demand is influencing the network design, explore current practice, and how we can build future networked infrastructure to better enable both efficient transfers of big data and low-latency requirements of real-time applications. The format of this course will be a mix of lectures, readings, discussions, assignments and reviews, a final exam, and a project. [Systems] Prerequisites: EN.601.226 and EN.601.414, or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming.

TuTh 3-4:15
limit 18

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
limit 25

601.421 (E)
CSCI-SOFT, CSCI-TEAM

OBJECT ORIENTED SOFTWARE ENGINEERING (3) Darvish

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 1:30-2:45
Sec 01: Th 1:30-2:45
Sec 02: Th 1:30-2:45
Sec 03: Th 1:30-2:45
Sec 04: Th 1:30-2:45
limit 19/section

601.428 (E)
CSCI-SOFT

COMPILERS & INTERPRETERS (3) Hovemeyer

Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. [Systems]>

Prereq: 600.120/601.220 and 600.226/601.226 and 600.233/601.229; 600.271/601.231 recommended

MW 12-1:15
limit 24

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. Required. 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]

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 5

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 9

601.441 (E)
CSCI-THRY

BLOCKCHAINS AND CRYPTOCURRENCIES Green

This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. [Analysis] Students may receive credit for only one of 600.451, 601.441, 601.641.

Prereq: 601.226 and (EN.553.310 or EN.553.410 probability).

MW 12-1:15
limit 24

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 10

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.

Tu 9-10:10, Th 9-10:20
limit 19

601.447 (E)
CSCI-APPL, CSCI-TEAM

COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead

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 9-10:15
limit 24

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 24

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 29

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 12-1:15
limit 39

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 10

601.464 (E)
CSCI-RSNG

ARTIFICIAL INTELLIGENCE (3) Mielke

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

Sec 02: TuTh 10:30-11:45
limit 39

601.465 (E)
CSCI-APPL

NATURAL LANGUAGE PROCESSING (4) Eisner

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 3-4:15
limit 38

601.467 (E)
CSCI-APPL

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 40

601.468 (E)
CSCI-APPL

MACHINE TRANSLATION (3) Koehn

Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. [Applications]

Required course background: prob/stat, 600/601.226. Student may receive credit for at most one of 601.468/668.

TuTh 1:30-2:45
limit 18

601.475 (E)
CSCI-RSNG

MACHINE LEARNING (3) Dredze/Wood-Doughty

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.

01 (limit 45): MW 1:30-2:45, sect F 1:30-2:45
02 (limit 25): MW 3-4:15, sect F 1:30-2:45

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 10

601.482 (E)
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING (4) Unberath

Deep learning (DL) has emerged as a powerful tool for solving data-intensive learning problems such as supervised learning for classification or regression, dimensionality reduction, and control. As such, it has a broad range of applications including speech and text understanding, computer vision, medical imaging, and perception-based robotics.
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: (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.

MWF 12-1:15p
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

601.614
CSCI-SYST

COMPUTER NETWORKS Jin

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.

TuTh 1:30-2:45
limit 24

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

601.618
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 15

601.619
CSCI-SYST

CLOUD COMPUTING Ghorbani

[Same as 601.419, for graduate students.] Clouds host a wide range of applications that we rely on today. In this course, we study foundational cloud systems and infrastructures, common cloud applications and the traffic patterns that they generate, and core networking and distributed systems concepts and algorithms that enable cloud computing. We will also study how today's application demand is influencing the network design, explore current practice, and how we can build future networked infrastructure to better enable both efficient transfers of big data and low-latency requirements of real-time applications. The format of this course will be a mix of lectures, readings, discussions, assignments and reviews, a final exam, and a project. [Systems]

Required course background: EN.601.226 Data Structures and EN.601.414/614 Computer Networks. Students can only receive credit for one of 601.419/619.

TuTh 3-4:15
limit 20

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
limit 40

601.621
CSCI-SOFT

OBJECT ORIENTED SOFTWARE ENGINEERING Darvish

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 1:30-2:45
Sec 01: Th 1:30-2:45
limit 25

601.628
CSCI-SOFT

COMPILERS & INTERPRETERS Hovemeyer

Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. [Systems]

Prereq: 600.120/601.220 and 600.226/601.226 and 600.233/601.229; 600.271/601.231 recommended

MW 12-1:15
limit 24

601.631
CSCI-THRY

THEORY OF COMPUTATION (3) Li

This is a graduate-level course studying the theoretical foundations of computer science. Topics covered will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.631, unless one is for an undergrad degree and the other for grad. [Analysis]

Prereq: discrete math or permission.

TuTh 12-1:15
limit 29

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 40

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 24

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 20

601.641
CSCI-THRY

BLOCKCHAINS AND CRYPTOCURRENCIES Green

[Cross-listed in JHUISI; Same as EN.601.441, for graduate students.] This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. [Analysis] Students may receive credit for only one of 600.451, 601.441, 601.641.

Required course background: 601.226 and probability (any course).

MW 12-1:15
limit 24

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 19

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.

Tu 9-10:10, Th 9-10:20
limit 19

601.647
CSCI-APPL

COMPUTATIONAL GENOMICS: SEQUENCES Langmead

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 9-10:15
limit 24

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 38

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 1:30
limit 20

601.661
CSCI-APPL

COMPUTER VISION Hager

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 12-1:15
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 19

601.665
CSCI-APPL

NATURAL LANGUAGE PROCESSING Eisner

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 3-4:15
limit 45

601.667 (E)
CSCI-APPL

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 50

601.668
CSCI-APPL

MACHINE TRANSLATION Koehn

Same material as 601.468, for graduate students. [Applications]

Required course background: prob/stat, 600.226. Student may receive credit for at most one of 601.468/668.

TuTh 1:30-2:45
limit 30

601.675
CSCI-RSNG

MACHINE LEARNING Dredze/Wood-Doughty

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.

01 (limit 65): MW 1:30-2:45, sect F 1:30-2:45
02 (limit 24): MW 3-4:15, sect F 1:30-2:45

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 19

601.682
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING Unberath

Same as 601.482, for graduate students. [Applications]

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

MWF 12-1:15p
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
limit 20 (CS grad students or permission)

601.780

CSCI-REAS
ADDED!

UNSUPERVISED LEARNING: FROM BIG DATA TO LOW-DIMENSIONAL REPRESENTATIONS Vidal

(Previously ADVANCED TOPICS IN MACHINE LEARNING: MODELING & SEGMENTATION OF MULTIVARIATE MIXED DATA.) In the era of data deluge, the development of methods for discovering structure in high-dimensional data is becoming increasingly important. This course will cover state-of-the-art methods from algebraic geometry, sparse and low-rank representations, and statistical learning for modeling and clustering high-dimensional data. The first part of the course will cover methods for modeling data with a single low-dimensional subspace, such as PCA, Robust PCA, Kernel PCA, and manifold learning techniques. The second part of the course will cover methods for modeling data with multiple subspaces, such as algebraic, statistical, sparse and low-rank subspace clustering techniques. The third part of the course will cover applications of these methods in image processing, computer vision, and biomedical imaging. [Applications]

Required course background: linear algebra, optimization and statistics. Prior exposure to machine learning (e.g., 601.475/675) is a plus.

TuFr 3-4:15p
limit 35

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

601.814

SELECTED TOPICS IN COMPUTER NETWORKS Jin

In this course we will read, discuss and present classic papers and current research in computer networks. The topic coverage will vary each semester.

W 4-5
limit 15

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

601.819

SELECTED TOPICS IN CLOUD COMPUTING AND NETWORKED SYSTEMS Ghorbani

Participants will read and discuss seminal and recent foundational research on cloud and networked systems.

Fr 4-5p
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 Eisner

A reading group exploring important current research in the field and potentially relevant material from related fields. Enrolled students are expected to present papers and lead discussion.

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

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
04 - Russ Taylor (ugrad research use 517, not 507)
05 - Scott Smith
06 - Joanne Selinski
07 - Harold Lehmann
08 - staff [Joao Sedoc]
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
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 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 - Mihaela Pertea
61 - Archana Venkataraman 
62 - Matt Post