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 nondepartment 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 and affiliated MSE programs only (differs by course). After the initial registration period for each group, these restrictions will be lifted  on or about Dec. 3rd for undergraduate courses and January 9th for graduate courses. 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 were 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:
 CSCIAPPL Applications
 CSCIRSNG Reasoning
 CSCISOFT Software
 CSCISYST Systems
 CSCITHRY Theory
 CSCITEAM Team (undergraduate only)
 CSCIETHS 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 colisted 601.4xx/6xx courses are equivalent.]
Courses without end times are presumed to meet for 50 minute periods. Final room assignments will be available on the Registrar's website in January. Changes to the original SISposted schedule are noted in red.
500.112 (E) 
GATEWAY COMPUTING: JAVA (3) staff This course introduces fundamental programming concepts and techniques, and is intended for all who plan to develop computational artifacts or intelligently deploy computational tools in their studies and careers. Topics covered include the design and implementation of algorithms using variables, control structures, arrays, functions, files, testing, debugging, and structured program design. Elements of objectoriented programming, algorithmic efficiency and data visualization are also introduced. Students deploy programming to develop working solutions that address problems in engineering, science and other areas of contemporary interest that vary from section to section. Course homework involves significant programming. Attendance and participation in class sessions are expected. 
MWF 50 minutes, limit 19/section 
601.104 (H) 
COMPUTER ETHICS (1) Leschke Students will examine a variety of topics regarding policy, legal, and moral issues related to the computer science profession itself and to the proliferation of computers in all aspects of society, especially in the era of the Internet. The course will cover various general issues related to ethical frameworks and apply those frameworks more specifically to the use of computers and the Internet. The topics will include privacy issues, computer crime, intellectual property law  specifically copyright and patent issues, globalization, and ethical responsibilities for computer science professionals. Work in the course will consist of weekly assignments on one or more of the readings and a final paper on a topic chosen by the student and approved by the instructor. 
Sections meet during the first 8 weeks of the semester only.
Sec 01: Mon 4:306:00p 
601.124 (EH) 
THE ETHICS OF ARTIFICIAL INTELLIGENCE & AUTOMATION (3) LopezGonzalez The expansion of artificial intelligence
(AI)enabled use cases across a broad spectrum of domains has
underscored the benefits and risks of AI. This course will address the
various ethical considerations engineers need to engage with to build
responsible and trustworthy AIenabled autonomous systems. Topics to
be covered include: valuesbased decision making, ethically aligned
design, cultural diversity, safety, bias, AI explainability, privacy,
AI regulation, the ethics of synthetic life, and the future of
work. Case studies will be utilized to illustrate realworld
applications. Students will apply learned material to a group research
project on a topic of their choice. 
Sec 01: MW 121:15p 
601.220 (E)

INTERMEDIATE PROGRAMMING (4) Darvish, Hovemeyer This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover lowlevel programming techniques, as well as objectoriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. The course includes usage of Linux tools for file manipulation, editing and programming, and git for versioning. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages. Prereq: 500.132/133/134 OR (C+/S*/S** or better grade in 500.112/113/114) or AP CS or equivalent. 
Sec 01 (Darvish): MWF 10:0011:15 
601.226 (EQ) 
DATA STRUCTURES (4) Madooei, Simari This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: 500.132 OR (C+/S*/S** or better grade in 500.112 or 601.220) or AP CS credit or equivalent. 
Sec 01 (Madooei): MWF 121:15p 
601.229 (E) 
COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer This course covers modern computer systems from a software perspective. Topics include binary data representation, machine arithmetic, assembly language, computer architecture, performance optimization, memory hierarchy and cache organization, virtual memory, Unix systems programming, network programming, and concurrency. Hardware and software interactions relevant to computer security are highlighted. Students will gain handson experience with these topics in a series of programming assignments. Prereq: 601.220. 
MWF 10a, limit 120 
601.230 (EQ) 
MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE (4) More This course provides an introduction to mathematical reasoning and discrete structures relevant to computer science. Topics include propositional and predicate logic, proof techniques including mathematical induction, sets, relations, functions, recurrences, counting techniques, simple computational models, asymptotic analysis, discrete probability, graphs, trees, and number theory. Pre/coreq: Gateway Computing (500.112/113/114/132/133/134 or AP CS or 601.220). Students can get credit for at most one of EN.601.230 or EN.601.231. 
Sec 01: MWF 99:50a, W 4:305:20p 
601.290 (E) 
USER INTERFACES AND MOBILE APPLICATIONS (3) Selinski This course will provide students with a rich development experience, focused on the design and implementation of user interfaces and mobile applications. A brief overview of human computer interaction will provide context for designing, prototyping and evaluating user interfaces. Students will invent their own mobile applications and implement them using the Android SDK, which is JAVA based. An overview of the Android platform and available technologies will be provided, as well as XML for layouts, and general concepts for effective mobile development. Students will be expected to explore and experiment with outside resources in order to learn technical details independently. There will also be an emphasis on building teamwork skills, and on using modern development techniques and tools. [Oral] Prereq: 601.220 and 601.226. 
MW 34:15 
601.350 (E) 
GENOMIC DATA SCIENCE (3) Salzberg [Formerly Intro to Genomic Research] This course will use a projectbased approach to introduce undergraduates to research in computational biology and genomics. During the semester, students will take a series of large data sets, all derived from recent research, and learn all the computational steps required to convert raw data into a polished analysis. Data challenges might include the DNA sequences from a bacterial genome project, the RNA sequences from an experiment to measure gene expression, the DNA from a human microbiome sequencing experiment, and others. Topics may vary from year to year. In addition to computational data analysis, students will learn to do critical reading of the scientific literature by reading highprofile research papers that generated groundbreaking or controversial results. [Applications] Prerequisites: knowledge of the Unix operating system and programming expertise in a language such as Perl or Python.

TuTh 34:15 
601.356 (E) 
COMPUTER INTEGRATED SURGERY SEMINAR (1) Taylor Lecture only version of 601.456 (no project). Prereq: 601.455 or perm req'd. Students may receive credit for 600.356 or 600.456, but not both. 
TuTh 1:302:45 
601.404 (E) 
BRAIN & COMPUTATION (1) Kosaraju Computational and network aspects of the brain are explored. The topics covered include structure, operation and connectivity of neurons, general network structure of the neural system, and the connectivity constraints imposed by pre and postnatal neural development and the desirability of network consistency within a species. Both discrete and continuous aspects of neural computation are covered. Precise mathematical tools and analyses such as logic design, transient and steady state behavior of linear systems, and time and connectivity randomization are discussed. The concepts are illustrated with several applications. Memory formation from the synaptic level to the high level constructs are explored. Students are not expected to master any of the mathematical techniques but are expected to develop a strong qualitative appreciation of their power. Cerebellum, which has a simple network connectivity, will be covered as a typical system. Prerequisites: linear algebra, differential equations, probability, and algorithms; or instructor approval. Students can receive credit for EN.601.404 or EN.601.604, but not both. 
Tu 4:305:20p 
601.411 (E) 
CS INNOVATION AND ENTREPRENEURSHIP II (3) Dahbura & Aronhime This course is the second half of a twocourse sequence and is a continuation of course 660.410.01, CS Innovation and Entrepreneurship, offered by the Center for Leadership Education (CLE). In this sequel course the student groups, directed by CS faculty, will implement the business idea which was developed in the first course and will present the implementations and business plans to an outside panel made up of practitioners, industry representatives, and venture capitalists. Prerequisite: 660.410. 
MW 121:15p 
601.413 (E) 
SOFTWARE DEFINED NETWORKS (3) Sabnani
SoftwareDefined Networks (SDN) enable programmability of data
networks and hence rapid introduction of new services. They use
softwarebased controllers to communicate with underlying
hardware infrastructure and direct traffic on a network. This
model differs from that of traditional networks, which use
dedicated hardware devices (i.e., routers and switches) to
control network traffic. Prerequisite: EN.601.414/614. Students can receive credit for EN.601.413 or EN.601.613, but not both. 
Tu 4:307p 
601.419 (E) 
CLOUD COMPUTING (3) Ghorbani Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and lowlatency requirements of realtime applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. [Systems] Prerequisites: EN.601.226 and EN.601.414 or permission. Students can only receive credit for one of 601.419/619. 
MW 34:15p 
601.421 (E) 
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Madooei This course covers objectoriented 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 objectoriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [(Systems or Applications), Oral] Prereq: 601.226 & 601.220 & (EN.601.280 or EN.601.290). Students may receive credit for only one of 601.421/621. 
MWF 4:305:20p 
601.422 (E) 
SOFTWARE TESTING & DEBUGGING (3) Darvish Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntaxbased coverage), unit testing, higherorder testing (integration, systemlevel, acceptance), testing approaches (whitebox, blackbox, greybox), regression testing, debugging, delta debugging, and several specific types of functional and nonfunctional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state oftheart tools/techniques will be studied and utilized. [Systems] Prereq: EN.601.290 or EN.601.421. Students may receive credit for 601.422 or 601.622, but not both. 
MWF 1:302:20p 
601.426 (EQ) 
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Functional, objectoriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. [Analysis] Required course background: 601.226. Freshmen and sophomores by permission only. 
MW 1:302:45 
601.433 (EQ) 
INTRO ALGORITHMS (3) Garg
This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as unionfind); graph algorithms and searching techniques such as minimum spanning trees, depthfirst search, shortest paths, design of online algorithms and competitive analysis. [Analysis] Prereq: 601.226 and (553.171/172 or 601.231 or 601.230) or Perm. Req'd. Students may receive credit for only one of 601.433/633. 
Sec 01: MW 121:15 
601.441 (E)

BLOCKCHAINS AND CRYPTOCURRENCIES Jain & 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 probability (EN.553.211/EN.553.310/EN.553.311/EN.553.420/EN.560.348). 
MW 121:15 
601.446 (E) 
SKETCHING & INDEXING FOR SEQUENCES (3) Langmead Many of the world's largest and fastestgrowing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing  succinct and compressed indexes in particular  which enables us to efficiently search inside very long strings, especially in highly repetitive texts. [Analysis] Prereq: 600/601.226. Students may receive credit for 601.446 or 601.646, but not both. 
TuTh 9:0010:15 
601.453 (E) 
APPLICATIONS OF AUGMENTED REALITY (3) MartinGomez
This course is designed to expand the student’s augmented reality
knowledge and introduce relevant topics necessary for developing more
meaningful applications and conducting research in this field. The
course addresses the fundamental concepts of visual perception and
introduces nonvisual augmented reality modalities, including
auditory, tactile, gustatory, and olfactory applications. The
following sessions discuss the importance of integrating usercentered
design concepts to design meaningful augmented reality applications. A
later module introduces the basic requirements to design and conduct
user studies and guidelines on interpreting and evaluating the results
from the studies. During the course, students conceptualize, design,
implement and evaluate the performance of augmented reality solutions
for their use in industrial applications, teaching and training, or
healthcare settings. Homework in this course will relate to applying
the theoretical methods used for designing, implementing, and
evaluating augmented reality applications. Students may receive credit
for only one of 601.453/653. Prerequisites: EN.601.454/654. 
TuTh 1:302:45p 
601.454 (E) CSCIAPPL 
AUGMENTED REALITY (3) Azimi
Same as 601.654, for undergraduate students.
[Applications] Students may receive credit for only one of 601.454/654. Prerequisites: EN.601.220, EN.601.226, linear algebra. 
MW 3:004:15p 
601.456 (E) 
COMPUTER INTEGRATED SURGERY II (3) Taylor This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students who wish to use this course to satisfy the "Team" requirement should register for EN.601.496 instead. Students wishing to attend the weekly lectures as a 1credit seminar should sign up for 601.356. [Applications, Oral]
Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of
601.456, 601.496, 601.656. 
TuTh 1:302:45 
601.461 (EQ) 
COMPUTER VISION (3) Jones & Katyal This course gives an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3D 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 910:15a 
601.463 (E) 
ALGORITHMS FOR SENSORBASED 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 humanmachine systems. [Analysis] Prereq: 601.226, linear algebra, calculus, probability. Students may receive credit for only one of 601.463/663. 
TuTh 121:15 
601.464 (E) 
ARTIFICIAL INTELLIGENCE (3) Haque The course situates the study of Artificial Intelligence (AI) first in the broader context of Philosophy of Mind and Cognitive Psychology and then treats indepth methods for automated reasoning, automatic problem solvers and planners, knowledge representation mechanisms, game playing, machine learning, and statistical pattern recognition. The class is a recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as deduction, learning, and planning and navigation. Strong programming skills and a good grasp of the English language are expected; students will be asked to complete both programming assignments and writing assignments. The course will include a brief introduction to scientific writing and experimental design, including assignments to apply these concepts. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 
TuTh 3:004:15p 
601.466 (E) 
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky An indepth, handson study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. [Applications] Required course background: 601.226. 
TuTh 34:15 
601.471 (E) 
NLP: SELFSUPERVISED MODELS (3) Khashabi The rise of massive selfsupervised (pretrained) models have transformed various datadriven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to selfsupervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own selfsupervised neural network models, using the Pytorch framework. Students may receive credit for EN.601.471 or EN.601.671, but not both. Prereqs: EN.601.226, one of (EN.601.464/664, EN.601.465/665, EN.601.467/667, EN.601.468/668, EN.601.475/675), Linear Algebra, and Probability, as well as familiarity with Python/PyTorch. 
TuTh 121:15p 
601.475 (E) 
MACHINE LEARNING (3) Arora
The goal of machine learning (a subfield of artificial intelligence)
is the development of computational systems, methods, and algorithms
that can learn from data to improve their performance. This course
introduces the foundational concepts of modern Machine Learning,
including core principles, popular algorithms and modeling platforms.
This will include both supervised learning, which includes popular
algorithms like SVMs, logistic regression and deep learning, as well
as unsupervised learning frameworks, which include Expectation
Maximization and graphical models. Homework assignments include both a
heavy programming components as well as analytical questions that
explore various machine learning concepts. This class will build on
prerequisites that include probability, linear algebra, multivariate
calculus and basic optimization.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus, probability, linear algebra. 
MWF 121:15p 
601.482 (E) 
MACHINE LEARNING: DEEP LEARNING (4) Unberath
Deep learning (DL) has emerged as a powerful tool for solving
dataintensive 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 perceptionbased
robotics. Prereq: EN.601.226 and (AS.110.201 or AS.110.212 or EN.553.291) and (EN.553.310 EN.553.311 or EN.553.420 or EN.560.348); numerical optimization and Python recommended. 
MW 4:305:45p, F 4:305:20p 
601.491 (E) 
HUMANROBOT INTERACTION (3) STAFF This course is designed to introduce advanced students to research methods and topics in humanrobot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledge of various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. [Applications] Prerequisite: EN.601.220 and EN.601.226. 
TuTh 34:15 
601.496 (E) 
COMPUTER INTEGRATED SURGERY II  TEAMS (3) Taylor This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project in teams of at least 3 students, under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students who prefer to do individual projects must register for EN.601.456 instead. [Applications, Oral]
Prereq: 601.455/655 or perm req'd. Students may receive credit for
only one of 601.456, 601.496, 601.656. 
TuTh 1:302:45 
601.501 
COMPUTER SCIENCE WORKSHOP An independent applicationsoriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal. Permission of faculty sponsor is required. 
See below for faculty section numbers. 
601.503 
UNDERGRADUATE INDEPENDENT STUDY Individual guided study for undergraduates, under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission of faculty sponsor is required.

See below for faculty section numbers. 
601.507 
UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. 
See below for faculty section numbers and whether to select 507 or 517. 
601.509 
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit, which is the limit per semester. Permission of faculty sponsor is required. 
See below for faculty section numbers. 
601.517 
GROUP UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. 
Only for faculty specifically marked below. 
601.520 
SENIOR HONORS THESIS (3) For computer science majors only, a continuation of 601.519. Prerequisite: 601.519 
See below for faculty section numbers. 
601.604 (E) 
BRAIN & COMPUTATION (1) Kosaraju Computational and network aspects of the brain are explored. The topics covered include structure, operation and connectivity of neurons, general network structure of the neural system, and the connectivity constraints imposed by pre and postnatal neural development and the desirability of network consistency within a species. Both discrete and continuous aspects of neural computation are covered. Precise mathematical tools and analyses such as logic design, transient and steady state behavior of linear systems, and time and connectivity randomization are discussed. The concepts are illustrated with several applications. Memory formation from the synaptic level to the high level constructs are explored. Students are not expected to master any of the mathematical techniques but are expected to develop a strong qualitative appreciation of their power. Cerebellum, which has a simple network connectivity, will be covered as a typical system. Required course background: linear algebra, differential equations, probability, and algorithms; or instructor approval. Students can receive credit for EN.601.404 or EN.601.604, but not both. 
Tu 4:305:20p 
601.611 
CS INNOVATION AND ENTREPRENEURSHIP II Dahbura & Aronhime Graduate level version of EN.601.411 (see for description) Prerequisites: 660.410. 
MW 121:15p 
601.613 (E) 
SOFTWARE DEFINED NETWORKS (3) Sabnani SoftwareDefined Networks (SDN) enable programmability of data networks and hence rapid introduction of new services. They use softwarebased controllers to communicate with underlying hardware infrastructure and direct traffic on a network. This model differs from that of traditional networks, which use dedicated hardware devices (i.e., routers and switches) to control network traffic. This technology is becoming a key part of web scale networks (at companies like Google and Amazon) and 5G/6G networks. Its importance will keep on growing. Many of today’s services and applications, especially when they involve the cloud, could not function without SDN. SDN allows data to move easily between distributed locations, which is critical for cloud applications. A major focus will be on how this technology will be used in 5G and 6G Networks. The course will cover basics of SDN, ongoing research in this area, and the industrial deployments. Required Course Background: computer networks. Students can receive credit for EN.601.413 or EN.601.613, but not both. 
Tu 4:307p 
601.619 
CLOUD COMPUTING Ghorbani [Same as 601.419, for graduate students.] Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and lowlatency requirements of realtime applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. [Systems] Required course background: EN.601.226 and EN.601.414/614 or permission. Students can only receive credit for one of 601.419/619. 
MW 34:15 
601.621 
OBJECT ORIENTED SOFTWARE ENGINEERING Madooei This course covers objectoriented 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 objectoriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [Systems or Applications] Required course background: intermediate programming, data structures, and experience in mobile or web app development. Students may receive credit for only one of 601.421/621. 
MWF 4:305:20p 
601.622 
SOFTWARE TESTING & DEBUGGING (3) Darvish Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntaxbased coverage), unit testing, higherorder testing (integration, systemlevel, acceptance), testing approaches (whitebox, blackbox, greybox), regression testing, debugging, delta debugging, and several specific types of functional and nonfunctional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state oftheart tools/techniques will be studied and utilized. [Systems] Prereq: EN.601.290 or EN.601.421 or EN.601.621. Students may receive credit for 601.422 or 601.622, but not both. 
MWF 1:302:20p 
601.626 
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Same as 601.426, for graduate stuents. Students may receive credit for only one of 601.426/626. [Analysis] Required course background: 601.226. 
MW 1:302:45 
601.633 
INTRO ALGORITHMS Garg
Same as 601.433, for graduate students. [Analysis] Prereq: 601.226 and (553.171/172 or 601.230 or 601.231) or Perm. Required. Students may receive credit for only one of 601.433/633. 
TuTh 121:15p 
601.641 
BLOCKCHAINS AND CRYPTOCURRENCIES Jain & Green [Crosslisted in JHUISI.] Same as EN.601.441, for graduate students. [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 121:15 
601.646 
SKETCHING & INDEXING FOR SEQUENCES (3) Langmead Many of the world's largest and fastestgrowing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing  succinct and compressed indexes in particular  which enables us to efficiently search inside very long strings, especially in highly repetitive texts. [Analysis] Prereq: Data Structures. Students may receive credit for 601.446 or 601.646, but not both. 
TuTh 910:15 
601.653 (E) 
APPLICATIONS OF AUGMENTED REALITY (3) MartinGomez
This course is designed to expand the student’s augmented reality
knowledge and introduce relevant topics necessary for developing more
meaningful applications and conducting research in this field. The
course addresses the fundamental concepts of visual perception and
introduces nonvisual augmented reality modalities, including
auditory, tactile, gustatory, and olfactory applications. The
following sessions discuss the importance of integrating usercentered
design concepts to design meaningful augmented reality applications. A
later module introduces the basic requirements to design and conduct
user studies and guidelines on interpreting and evaluating the results
from the studies. During the course, students conceptualize, design,
implement and evaluate the performance of augmented reality solutions
for their use in industrial applications, teaching and training, or
healthcare settings. Homework in this course will relate to applying
the theoretical methods used for designing, implementing, and
evaluating augmented reality applications. Students may receive credit
for only one of 601.453/653. Required course background: intermediate programming (C/C++), data structures, linear algebra; EN.601.654 preferred. 
TuTh 1:302:45p 
601.654 
AUGMENTED REALITY (3) Azimi
This course introduces students to the field of Augmented
Reality. It reviews its basic definitions, principles and
applications. It then focuses on Medical Augmented Reality and its
particular requirements. The course also discusses the main issues of
calibration, tracking, multimodal registration, advance visualization
and display technologies. Homework in this course will relate to the
mathematical methods used for calibration, tracking and visualization
in medical augmented reality. Students may also be asked to read
papers and implement various techniques within group
projects. [Applications] Students may receive credit for 600.484 or 600.684, but
not both. Required course background: intermediate programming (C/C++), data structures, linear algebra. 
MW 3:004:15p 
601.656 
COMPUTER INTEGRATED SURGERY II Taylor Same as 601.456, for graduate students. [Applications] Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of 601.456/496/656. 
TuTh 1:302:45 
601.661 
COMPUTER VISION Jones & Katyal 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 910:15 
601.663 
ALGORITHMS FOR SENSORBASED ROBOTICS Leonard Same as 601.463, for graduate students. [Analysis] Required course background: 601.226, linear algebra, calculus, probability. Students may receive credit for only one of 601.463/663. 
TuTh 121:15 
601.664 
ARTIFICIAL INTELLIGENCE Haque Same as 601.464, for graduate students. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 
TuTh 3:004:15p 
601.666 
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky Same material as 601.466, for graduate students. [Applications] Students may receive credit for at most one of 601.466/666. Required course background: 601.226. 
TuTh 34:15p 
601.671 (E) 
NLP: SELFSUPERVISED MODELS (3) Khashabi The rise of massive selfsupervised (pretrained) models have transformed various datadriven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to selfsupervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own selfsupervised neural network models, using the Pytorch framework. Students may receive credit for EN.601.471 or EN.601.671, but not both. Required course background: data structures, linear algebra, probability, familiarity with Python/PyTorch, natural language processing or machine learning. Prereqs: one of EN.601.464/664, EN.601.465/665, EN.601.467/667, EN.601.468/668, EN.601.475/675. 
TuTh 121:15p 
601.675 
MACHINE LEARNING Arora
Same as 601.475, for graduate students.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus, probability, linear algebra. 
MWF 121:15p 
601.682 
MACHINE LEARNING: DEEP LEARNING Unberath
Same as 601.482, for graduate students. [Applications]
Required course background: data structures, probability and linear algebra; numerical optimization and Python recommended. 
MW 4:305:45p, F 4:305:20p 
601.691 
HUMANROBOT INTERACTION STAFF This course is designed to introduce graduate students to research methods and topics in humanrobot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledge of various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. [Applications] Required course background: EN.601.220 and EN.601.226. 
TuTh 34:15 
601.740 
LANGUAGEBASED SECURITY Cao This course will introduce Languagebased Security, an emerging field in cyber security that leverages techniques from compilers and program analysis for securityrelated problems. Topics include but are not limited to: Controlflow and dataflow graphs, Program slicing, Code property graph (CPG), and Controlflow integrity. Students are expected to read new and classic papers in this area and discuss them in class. [Systems] Recommended Course Background: coursework in operating systems and preferably compilers. 
TuTh 12:001:15 
601.743 
ADVANCED TOPICS IN COMPUTER SECURITY Rubin [Crosslisted in ISI] Topics will vary from year to year, but will focus mainly on network perimeter protection, hostlevel protection, authentication technologies, intellectual property protection, formal analysis techniques, intrusion detection and similarly advanced subjects. Emphasis in this course is on understanding how security issues impact real systems, while maintaining an appreciation for grounding the work in fundamental science. Students will study and present various advanced research papers to the class. There will be homework assignments and a course project. [Systems or Applications] Prereq: college level security or crypto course; or permission of instructor. 
Mon 1:304:00 
EN.601.760

FFT IN GRAPHICS & VISION (3) Kazhdan In this course, we will study the Fourier Transform from the perspective of representation theory. We will begin by considering the standard transform defined by the commutative group of rotations in 2D and translations in two and threedimensions, and will proceed to the Fourier Transform of the noncommutative group of 3D rotations. Subjects covered will include correlation of images, shape matching, computation of invariances, and symmetry detection. [Applications or Analysis] Prereq: linear algebra and comfort with mathematical derivations. 
MW 1:302:45 
601.763 NEW COURSE! 
ADVANCED TOPICS IN ROBOT PERCEPTION Katyal The goal of this course is to explore machine learning and perception algorithms focused on robotic applications. Topics will include robot localization and mapping, pedestrian/obstacle detection/prediction, semantic segmentation, perceptionbased grasp planning, continual learning for perception algorithms and multimodal sensor fusion. This course will include introductions to the topics by the instructor followed by paper reading and discussions led by the students. In addition, this course will consist of an indepth semester long project that will emphasize research skills including developing a hypothesis, conducting literature reviews, formulating the problem, defining, and conducting experiments and finally evaluating and reporting results. [Applications] Required Course Background: Programming, Linear Algebra, Prob/Stat, Computer Vision and (Machine Learning or ML: Deep Learning). 
Fr 10a12:30p 
601.764 NEW COURSE! 
ADVANCED NLP: MULTILINGUAL METHODS Murray This is a project based course focusing on the design and implementation of systems that scale Natural Language Processing methods beyond English. The course will cover both multilingual and crosslingual methods with an emphasis on zeroshot and fewshot approaches, as well as ‘silver’ dataset creation. Modules will include CrossLingual Information Extraction & Semantics, CrossLanguage Information Retrieval, Multilingual Question Answering, Multilingual Structured Prediction, Multilingual Automatic Speech Recognition, as well as other nonEnglish centric NLP methods. Students will be expected to work in small groups and pick from one of the modules to create a model based on stateoftheart methods covered in the class. The course will be roughly twothirds lecture based and onethird students presenting project updates periodically throughout the semester. [Applications] Prerequisite: 601.465/665 NLP; Machine Translation recommended. 
TuTh 1:302:45 
601.778 (formerly 600.678) 
ADVANCED TOPICS IN CAUSAL INFERENCE (3) Shpitser This course will cover advanced topics on all areas of causal inference, including learning causal effects, pathspecific effects, and optimal policies from data featuring biases induced by missing data, confounders, selection, and measurement error, techniques for generalizing findings to different populations, complex probabilistic models relevant for causal inference applications, learning causal structure from data, and inference under interference and network effects. The course will feature a final project which would involve either an applied data analysis problem (with a causal inference flavor), a literature review, or theoretical work. [Analysis] Prerequisite: EN.601.477/677 or permission. 
TuTh 1:302:45p 
601.783 
VISION AS BAYESIAN INFERENCE (3) Yuille This is an advanced course on computer vision from a probabilistic and machine learning perspective. It covers techniques such as linear and nonlinear filtering, geometry, energy function methods, markov random fields, conditional random fields, graphical models, probabilistic grammars, and deep neural networks. These are illustrated on a set of vision problems ranging from image segmentation, semantic segmentation, depth estimation, object recognition, object parsing, scene parsing, action recognition, and text captioning. [Analysis or Applications] Required course background: calculus, linear algebra (AS.110.201 or equiv.), probability and statistics (AS.550.311 or equiv.), and the ability to program in Python and C++. Background in computer vision (EN.601.461/661) and machine learning (EN.601.475/675) suggested but not required. 
TuTh 910:15 
601.787 
ADVANCED MACHINE LEARNING: MACHINE LEARNING FOR TRUSTWORTHY AI Liu This course teaches advanced machine learning methods for the design, implementation, and deployment of trustworthy AI systems. The topics we will cover include but are not limit to different types of robust learning methods, fair learning methods, safe learning methods, and research frontiers in transparency, interpretability, privacy, sustainability, AI safety and ethics. Students will learn the stateoftheart methods in lectures, understand the recent advances by critiquing research articles, and apply/innovate new machine learning methods in an application. There will be homework assignments and a course project. Expected course background: 601.475/675 Machine Learning; recommended 601.476/676 ML: Data to Models and 601.482/682 Deep Learning. 
MW 34:15 
601.801 
Required for all CS PhD students. Strongly recommended for MSE students. Only 1st & 2nd year PhD students should formally register. 
TuTh 10:3011:45 
601.803 
MASTERS RESEARCH Independent research for masters or predissertation PhD students. Permission required. 
See below for faculty section numbers. 
601.805 
GRADUATE INDEPENDENT STUDY Permission Required. 
See below for faculty section numbers. 
601.807 
TEACHING PRACTICUM Selinski PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement. Permission required. 
limit 25, P/F only 
601.809 
PHD RESEARCH 
See below for faculty section numbers. 
601.810 
DIVERSITY & INCLUSION IN COMPUTER SCIENCE & ENGINEERING Kazhdan This reading seminar will focus on the question of diversity and inclusion in computer science (in particular) and engineering (in general). We aim to study the ways in which the curriculum, environment, and structure of computer science within academia perpetuates biases alienating female and minoritized students, and to explore possible approaches for diversifying our field. The seminar will meet on a weekly basis, readings will be assigned, and students will be expected to participate in the discussion. 
Wed 4:305:45p 
601.819 
SELECTED TOPICS IN CLOUD COMPUTING AND NETWORKED SYSTEMS Ghorbani Participants will read and discuss seminal and recent foundational research on cloud and networked systems. 
W 4:305:45p 
601.826 
SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith This seminar course covers recent developments in the foundations of programming language design and implementation. topics covered include type theory, process algebra, higherorder program analysis, and constraint systems. Students will be expected to present papers orally. 
Fri 1112 
601.856 
SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince This weekly seminar will focus on research issues in medical image analysis, including image segmentation, registration, statistical modeling, and applications. It will also include selected topics relating to medical image acquisition, especially where they relate to analysis. The purpose of the course is to provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups within the University. The format of the course is informal. Students will read selected papers. All students will be assumed to have read these papers by the time the paper is scheduled for discussion. But individual students will be assigned on a rotating basis to lead the discussion on particular papers or sections of papers. Colisted with 520.746. 
Tu 34:15 
601.857  SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. 
Tu 34:15 
601.864 added! 
SELECTED TOPICS IN MULTILINGUAL NATURAL LANGUAGE PROCESSING Yarowsky This is a weekly reading group focused on Natural Language Processing (NLP) outside of English. Whereas methods have gotten very strong in recent years on English NLP tasks, many methods fail on other languages due to both linguistic differences as well as lack of available annotated resources. This course will focus on CrossLanguage Information Retrieval, CrossLingual Information Extraction, Multilingual Semantics, Massively Multilingual Language Modeling, and other nonEnglish NLP subfields. Students will be expected to read, discuss, and present papers. Required course background: EN.601.465/665. 
M 1:30 
601.865 
SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Eisner A reading group exploring important current research in the field and potentially relevant material from related fields. Enrolled students are expected to present papers and lead discussion. Required course background: 600.465 or permission of instructor. 
Wed 121:15 
601.866 
SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme A seminar focussed on current research and survey articles on computational semantics. 
Fr 1010:50 
601.868 
SELECTED TOPICS IN MACHINE TRANSLATION Koehn Students in this course will review, present, and discuss current research in machine translation. Prereq: permission of instructor. 
M 1112 
500.745 
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Cowan, Whitcomb, Vidal, EtienneCummings Seminar series in robotics. Topics include: Medical robotics, including computerintegrated surgical systems and imageguided 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. Humanmachine systems, including haptic and visual feedback, human perception, cognition and decision making, and humanmachine collaborative systems. Crosslisted with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. 
Wed 121:30 
520.702 
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Trmal CLSP seminar series, for any students interested in current topics in language and speech processing. 
Mon & Fri 121:15 
650.624 
NETWORK SECURITY Johnston
This course focuses on communication security in computer systems and
networks. The course is intended to provide students with an
introduction to the field of network security. The course covers
network security services such as authentication and access control,
integrity and confidentiality of data, firewalls and related
technologies, Web security and privacy. Course work involves
implementing various security techniques. A course project is
required. 
TuTh 34:15p 
650.631 
ETHICAL HACKING Watkins Cyber security affects every facet of industry and our government, and thus is now a threat to National Security. This course is designed to introduce students to the skills needed to defend computer network infrastructure by exposing them to the handson identification and exploitation of vulnerabilities in servers (i.e., Windows and Linux), wireless networks, websites, and cryptologic systems. These skills will be tested by having teams of students develop and participate in instructor lead capturetheflag competitions. Also included are advanced topics such as shell coding, IDA Pro analysis, fuzzing, and writing or exploiting networkbased applications or techniques such as web servers, spoofing, and denial of service. 
Th 4:307p 
650.640 
MORAL & LEGAL FOUNDATIONS OF PRIVACY Matthew Welling This course explores the ethical and legal underpinnings of the concept of privacy. It examines the nature and scope of the right to privacy by addressing fundamental questions such as: What is privacy? Why is privacy morally important? How is the right to privacy been articulated in constitutional law? 
Tu 4:306:45p 
650.654 
COMPUTER INTRUSION DETECTION Li Intrusion detection supports the online monitoring of computer system activities and the detection of attempts to compromise normal services. This course starts with an overview of intrusion detection tasks and activities. Detailed discussion introduces a traditional classification of intrusion detection models, applications in hostcentered and distributed environments, and various intrusion detection techniques ranging from statistical analysis to biological computing. This course serves as a comprehensive introduction of recent research efforts in intrusion detection and the challenges facing modern intrusion detection systems. Students will also be able to pursue indepth study of special topics of interest in course projects. 
MW 121:15p 
650.667 
MOBILE DEVICE FORENSICS Leschke

Wed 6:309p 
650.672 
SECURITY ANALYTICS Zhang
Security analytics refers to information technology solutions that
gather and analyze security events to bring situational awareness and
enable IT staff to understand and analyze events that pose the
greatest risk. Increasingly, detecting and preventing cyber attacks
require sophisticated use of data analytics and machine learning
tools. This course will cover fundamental theories and methods in data
science, modern security analytical tools, and practical use cases of
security analytics. Students of this course learn concepts, tasks, and
methods of data science; and how to apply data science to cyber
security problems. Students also learn how to use modern software in
security analytics. 
Fri 4:15 6:45p 
650.837 
INFORMATION SECURITY PROJECTS Dahbura & Li Open to MSSI students. Permission Required for nonMSSI students. All MSSI programs must include a project involving a research and development oriented investigation focused on an approved topic addressing the field of information security and assurance from the perspective of relevant applications and/or theory. There must be project supervision and approval involving a JHUISI affiliated faculty member. A project can be conducted individually or within a teamstructured environment comprised of MSSI students and an advisor. A successful project must result in an associated report suitable for online distribution. When appropriate, a project can also lead to the development of a socalled "deliverable" such as software or a prototype system. Projects can be sponsored by government/industry partners and affiliates of the Information Security Institute, and can also be related to faculty research programs supported by grants and Contracts. Required for MSSI students on fulltime status. 
MW 1111:50a 
650.840 
INFORMATION SECURITY INDEPENDENT STUDY Li Individual study in an area of mutual interest to a graduate student and a faculty member in the Institute. 
P/F grades 
Faculty section numbers for all independent type courses, undergraduate and graduate.
01  Xin Li 02  Rao Kosaraju (emeritus) 03  Soudeh Ghorbani 04  Russ Taylor (ugrad research use 517, not 507) 05  Scott Smith 06  Joanne Selinski 07  Harold Lehmann [SPH] 08  Ali Madooei 09  Greg Hager (ugrad research use 517, not 507) 10  Craig Jones 11  Sanjeev Khudhanpur [ECE] 12  Yair Amir 13  David Yarowsky 14  Noah Cowan 15  Randal Burns 16  Jason Eisner (ugrad research use 517, not 507) 17  Mark Dredze 18  Michael Dinitz 19  Rachel Karchin [BME] 20  Michael Schatz 21  Avi Rubin 22  Matt Green 23  Yinzhi Cao 24  Raman Arora (ugrad research use 517, not 507) 25  Rai Winslow [BME] 26  Misha Kazhdan 27  David Hovemeyer 28  Ali Darvish 29  Alex Szalay [Physics] 30  Peter Kazanzides 31  Jerry Prince [BME] 32  Carey Priebe [AMS] 33  Nassir Navab 34  Rene Vidal [BME] 35  Alexis Battle (ugrad research use 517, not 507) [BME] 36  Emad Boctor (ugrad research use 517, not 507) [SOM] 37  Mathias Unberath 38  Ben VanDurme 39  Jeff Siewerdsen 40  Vladimir Braverman 41  Suchi Saria 42  Ben Langmead 43  Steven Salzberg 44  Jean Fan [BME] 45  Liliana Florea [SOM] 46  Casey Overby Taylor [SPH] 47  Philipp Koehn 48  Abhishek Jain 49  Anton Dahbura (ugrad research use 517, not 507) 50  Joshua Vogelstein [BME] 51  Ilya Shpitser 52  Austin Reiter 53  Tamas Budavari [AMS] 54  Alan Yuille 55  Peng Ryan Huang 56  Xin Jin 57  ChienMing Huang 58  Will Gray Roncal (ugrad research use 517, not 507) 59  Kevin Duh [CLSP] 60  Mihaela Pertea 61  Archana Venkataraman [ECE] 62  Matt Post [CLSP] 63  Vishal Patel [ECE] 64  Rama Chellappa [ECE] 65  Mehran Armand [MechE] 66  Jeremias Sulam [BME] 67  Anqi Liu 68  Yana Safanova 69  Musad Haque 70  Stephen Walli 71  Gregory Falco [CaSE] 72  Thomas Lippincott [CLSP] 73  Joel Bader [BME] 74  Daniel Khashabi 75  Nicolas Loizou (AMS) 76  Alejandro Martin Gomez 77  Kenton Murray 78  Ehsan Azimi [SoN] 79  Krishan Sabnani