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Brown University PhD in Computer Science

Computer Science is a concentration offered under the computer science major at Brown University. We’ve pulled together some essential information you should know about the doctor’s degree program in computer science, including how many students graduate each year, the ethnic diversity of these students, and more.

If there’s something special you’re looking for, you can use one of the links below to find it:

  • Graduate Cost
  • Online Learning
  • Student Diversity

Featured Programs

Learn about start dates, transferring credits, availability of financial aid, and more by contacting the universities below.

AS in Computer Science

Learn the applied programming skills needed to fill in-demand tech roles when you earn your online AS in Computer Science at Southern New Hampshire University.

Southern New Hampshire University Logo

BS in Computer Science

Learn the front-end design and back-end development skills employers look for in full stack software developers with this online bachelor's degree in computer science from Southern New Hampshire University.

BS in Computer Science - Software Engineering

With a software engineering degree, you'll learn the fundamental concepts and principles – a systematic approach used to develop software on time, on budget and within specifications – throughout your online college classes at SNHU.

How Much Does a Doctorate in Computer Science from Brown Cost?

Brown graduate tuition and fees.

During the 2019-2020 academic year, part-time graduate students at Brown paid an average of $0 per credit hour. No discount was available for in-state students. Information about average full-time graduate student tuition and fees is shown in the table below.

Related Programs

Learn about other programs related to <nil> that might interest you.

MS in Information Technology - Software Application Development

Learn to manage the development process for a software program with this specialized online master's from Southern New Hampshire University.

Does Brown Offer an Online PhD in Computer Science?

Brown does not offer an online option for its computer science doctor’s degree program at this time. To see if the school offers distance learning options in other areas, visit the Brown Online Learning page.

Brown Doctorate Student Diversity for Computer Science

Male-to-female ratio.

About 44.4% of the students who received their PhD in computer science in 2019-2020 were women. This is higher than the nationwide number of 19.1%.

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Racial-Ethnic Diversity

Of those graduates who received a doctor’s degree in computer science at Brown in 2019-2020, 11.1% were racial-ethnic minorities*. This is about the same as the nationwide number of 10%.

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*The racial-ethnic minorities count is calculated by taking the total number of students and subtracting white students, international students, and students whose race/ethnicity was unknown. This number is then divided by the total number of students at the school to obtain the racial-ethnic minorities percentage.

  • National Center for Education Statistics
  • O*NET Online

More about our data sources and methodologies .

Popular Reports

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Ph.D. Students - Electrical and Computer Engineering

  • Graduate Students
  • Ph.D. Students

Abdelatty

Manar Abdelatty

Adamson

Zachary Adamson

Mahdi Boulila

Mahdi Boulila

Chiang-Heng Chien

Chiang-Heng Chien

Jeffrey Daulton

Jeffrey Daulton

A. nicole dusang.

Zhaoji Fang

Zhaoji Fang

Cole Foster

Cole Foster

Lee

Kuan-Min Lee

Jeffrey Lei

Jeffrey Lei

Xiaoyu Lian

Xiaoyu Lian

Litterio

Gabby Litterio

Jingxiao Ma

Jingxiao Ma

Pingchuan Ma

Pingchuan Ma

Miles Miller-Dickenson

Miles Miller-Dickson

Akshay Nagar

Akshay Nagar

Marina Neseem

Marina Neseem

Joseph Plumitallo

Joseph Plumitallo

yaseman shiri

Yaseman Shiri

karpur shukla

Karpur Shukla

Jieliyue Sun

Jieliyue Sun

Ayan Waite

Theoretical Computer Science

Brown university, department of computer science.

Welcome to Theoretical Computer Science at Brown.

Faculty Members

We are proud to have a National Academy of Engineering mnember and Gödel prize winner (Maurice Herlihy), two AAAS Fellows (John Savage and Roberto Tamassia), six ACM Fellows (Maurice Herlihy, Philip Klein, Franco Preparata, John Savage, Roberto Tamnassia, and Eli Upfal) and four IEEE Fellows (Franco Preparata, John Savage, Roberto Tamnassia, and Eli Upfal).

  • Yu Cheng : Algorithms, machine learning
  • Amy Greenwald : Artificial intelligence
  • Maurice Herlihy : Distributed and parallel computing
  • Ellis Hershkowitz : Graph algorithms, metric embeddings
  • Sorin Istrail : Computational biology, algorithms
  • Seny Kamara : Security and cryptography
  • Philip Klein : Algorithms on graphs and networks
  • Anna Lysyanskaya : Cryptography
  • Peihan Miao : Cryptography and Security
  • Franco Preparata : Combinatorial computing, algorithms, computational biology
  • John Savage : Nanotechnology
  • Roberto Tamassia : Security and cryptography, algorithms
  • Eli Upfal : Algorithms, probability and applications
  • Suresh Venkatasubramanian : Algorithmic fairness, machine learning

Faculty friends

Theory seminar.

The theory group organizes weekly theory seminars. For talk schedule and more details, please visit this link .

PhD Students

  • Pinar Demetci
  • Scott Griffy
  • Victor Youdom Kemmoe
  • Lizzie Kumar
  • Kweku Kwegyir-Aggrey
  • Evangelia Anna (Lilika) Markatou
  • Alessio Mazzetto
  • Pegah Nokhiz
  • Leah Namisa Rosenbloom
  • Kevin A. Wang

PhD/Postdoc Alumni (since 2006)

  • Archita Agarwal (PhD 2021; Denison University)
  • Cyrus Cousins (PhD 2021; University of Massachusetts Amherst)
  • Jasper Lee (PhD 2021; University of Wisconsin–Madison)
  • Megumi Ando (PhD 2020; MITRE)
  • Lorenzo De Stefani (PhD 2020; Brown University)
  • Amy Becker (PhD 2019)
  • Elizabeth Crites (PhD 2019; University of Edinburgh)
  • Apoorvaa Deshpande (PhD 2019; Snap)
  • Thomas Dickerson (PhD 2019; Geopipe)
  • Evgenios Kornaropoulos (PhD 2019; George Mason University)
  • Vikram Saraph (PhD 2019; JHU Applied Physics Laboratory)
  • Esha Ghosh (PhD 2018; Microsoft Research)
  • Zhiyu Liu (PhD 2017)
  • Ahmad Mahmoody (PhD 2017; Snap)
  • Alessandro Epasto (Postdoc 2016; Google)
  • James Kelley (PhD 2015; Akamai)
  • Irina Calciu (PhD 2015; Graft)
  • Hammurabi Mendes (PhD 2015; Davidson College)
  • Derek Aguiar (PhD 2014; University of Connecticut)
  • Foteini Baldimtsi (PhD 2014; George Mason University)
  • David Eisenstat (PhD 2014; Google Research)
  • Matteo Riondato (PhD 2014; Amherst College)
  • Feng-Hao Liu (PhD 2013; Florida Atlantic University)
  • Olga (Olya) Ohrhimenko (PhD 2013; University of Melbourne)
  • Shay Mozes (PhD 2012; Reichman University)
  • Ryan Tarpine (PhD 2012; Google)
  • Alper Uzun (Postdoc 2012; Brown University)
  • Austin Huang (Postdoc 2011; Google Brain)
  • Fumei Lam (Postdoc 2011)
  • Charalampos (Babis) Papamanthou (PhD 2011; Yale University)
  • Aparna Das (PhD 2010; Le Moyne College)
  • Alptekin Küpçü (PhD 2010; Koç University)
  • Yossi Lev (PhD 2010; Intel)
  • Eric Rachlin (PhD 2010; Amazon)
  • Warren Schudy (PhD 2010; Google Research)
  • Mira Belenkiy (PhD 2008; Gradient)
  • Melissa Chase (PhD 2008; Microsoft Research)
  • Glencora Borradaile (PhD 2007; Oregon State University)
  • Danfeng (Daphne) Yano (PhD 2007; Virginia Tech)
  • Aris Anagnostopoulos (PhD 2006; Sapienza University of Rome)
  • Nikos Triandopoulos (PhD 2006; Stevens Institute of Technology)

Courses Recently Offered

  • CSCI1450 : Introduction to Probability and Computing
  • CSCI1510 : Introduction to Cryptography and Computer Security
  • CSCI1515 : Applied Cryptography
  • CSCI1550 : Probabilistic Methods in Computer Science
  • CSCI1570 : Design and Analysis of Algorithms
  • CSCI1810 : Computational Molecular Biology
  • CSCI1820 : Algorithmic Foundations of Computational Biology
  • CSCI1950-H : Computational Topology
  • CSCI2500-B : Optimization Algorithms for Planar Graphs
  • CSCI2510 : Approximation Algorithms
  • CSCI2730 : Programming Language Theory
  • CSCI2750 : Topics in Parallel and Distributed Computing
  • CSCI2840 : Advanced Algorithms in Computational Biology and Medical Bioinformatics
  • CSCI2950-C : Algorithms for Cancer Genomics
  • CSCI2951-L : Special Topics in Secure Computation
  • CSCI2951-S : Distributed Computing through Combinatorial Topology

See the full course listing here .

Data Science Institute

Data science institute at brown.

The mission of the Data Science Institute (DSI) at Brown is to stimulate innovation and support people aspiring to improve lives in our data-driven world.

DSI engages people across campus and beyond, to: 

  • Educate all in data fluency and advanced area-specific applications of data science methods 
  • Stimulate large-scale multidisciplinary research developing and applying data science methods to multiple data modalities
  • Ensure that the power of data be leveraged toward a more equitable society

Opportunities at DSI

  • Faculty Position: Professor of the Practice of Data Science

Upcoming Events

Recent news, best paper award for dsi's cristina menghini and cs colleagues stephen bach and yong zheng-xin, old concept, new implications: brown scholars interrogate how ai is changing the world, dsi's scientific machine learning research group gets published in nature, academic programs.

phd in computer science at brown university

Undergraduate Programs

Data Science options for undergraduates include a Certificate in Data Fluency and the Data Science Fellows program trains undergraduates to collaborate with faculty to build more data science into course curricula.

phd in computer science at brown university

Graduate Programs

DSI offers a master's degree in Data Science and a doctoral certificate, designed for students from a broad range of educational and work backgrounds.

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Since our inception in 1979, the Computer Science Department at Brown has forged a path of innovative information technology research and teaching at both the undergraduate and graduate levels. From our modest beginnings as an interest group within the Divisions of Applied Mathematics and Engineering in the 1960s to its current stature as one of the nation's leading computer science programs, the Computer Science Department has continuously produced prominent contributors in the field. Computer Science combines the intellectual challenge of a new discipline with the excitement of an innovative and rapidly expanding technology. The department resides in Brown’s Center for Information Technology; this striking building houses many of the university’s computing activities, as well as the department’s instructional computing facilities and research labs. Faculty, staff and students are provided state-of-the-art computing facilities.

We are a diverse community of scholars engaged in all aspects of research, teaching and mentoring in computer science and its related interdisciplinary disciplines. Realizing the importance of computing and algorithmic thinking in so many scientific, social and technological endeavors, we collaborate extensively with colleagues in archaeology, applied mathematics, biology, cognitive and linguistic sciences, economics, engineering, mathematics, medicine, physics and neuroscience.

Our undergraduate offerings reflect the department's multidisciplinary orientations, with joint concentrations in mathematics, applied mathematics, computational biology and economics. We have strong undergraduate research groups and a long history of involving undergraduates in projects that span disciplinary boundaries. 

For additional information, please visit the department's website:   https://www.cs.brown.edu/

Course usage information

CSCI 0020. The Digital World .

Removes the mystery surrounding computers and the ever-growing digital world. Introduces a range of topics and many aspects of multimedia, along with explanations of the underlying digital technology and its relevance to our society. Other topics include artificial intelligence, IT security, ethics and the economics of computing as well as the effects of its pervasiveness in today's world. Introductory programming and analytic skills are developed through Excel, HTML, CSS, Javascript, and Python assignments. CSCI0020 is a good introduction to a wide range of CS topics that have broad relevance in our society. No prerequisites. Cannot be taken to fulfill CS Concentration.

CSCI 0030. Introduction to Computation for the Humanities and Social Sciences .

Introduces students to the use of computation for solving problems in the social sciences and the humanities. We will investigate a series of real-world problems taken from the news, from books such as Freakonomics, and from current research. Topics covered include data gathering, analysis, and visualization; web-based interfaces; algorithms; and scripting. Enrollment limited to 20. Instructor permission required.

CSCI 0040. Introduction to Scientific Computing and Problem Solving .

CSCI0040 provides an introduction to using computers to solve STEM (Science, Technology, Engineering and Mathematics) data analysis, visualization and simulation problems from engineering, neuroscience, biology, mathematics and finance. Students will access and analyze a number of "real world" data sets while becoming fluent MATLAB programmers. Other tools utilized may include Excel, Wolframalpha and Python. By course end, students should be able to use MATLAB to solve a large variety of scientific data analysis, visualization and simulation problems. No prior programming experience is required (MATLAB is easy and fun to use).

CSCI 0050. A Data-Centric Introduction to Programming .

An introduction to computer programming with a focus on skills needed for data-intensive applications. Topics include core constructs for processing both tabular and structured data; decomposing problems into programming tasks; data structures; algorithms; and testing programs for correct behavior.

CSCI 0060. Practical System Skills .

An introduction to develop hands-on-computing skills necessary to comfortably work within a UNIX-like operating system. Topics include the shell, its filesystem, bash scripting, SSH, version control, as well as how to locally develop, deploy and publish a website. https://cs.brown.edu/courses/csci0060/

CSCI 0080. A First Byte of Computer Science .

Introduces non-CS concentrators to the academic discipline of computer science, its thought processes, and its relevance to other fields and modern life more generally. The target audience is students who are interested in learning more about what computer science is about and the ideas it has to offer tomorrow's citizens and scholars. Topics include the basics of computation and programming, a taste of theoretical computer science and algorithms, and an introduction to codes and artificial intelligence. Although students will learn to read and understand short programs, the course will not teach or require advanced programming skills.

CSCI 0081. TA Apprenticeship: Full Credit .

Being an undergraduate TA is a learning experience: one not only gets a deeper understanding of the course material, but gains management and social skills that are invaluable for one's future. Students taking this course must first be selected as an undergraduate TA for a Computer Science course, a course the student has taken and done well in. Students will work with the course's instructor on a variety of course-related topics, including preparation of material and development of assignments. Whether CSCI 0081 or its half-credit version ( CSCI 0082 ) is taken is up to the professor of the course being TA'd. Instructor permission required.

CSCI 0082. TA Apprenticeship: Half Credit .

Being an undergraduate TA is a learning experience: one not only gets a deeper understanding of the course material, but gains management and social skills that are invaluable for one's future. Students taking this course must first be selected as an undergraduate TA for a Computer Science course, a course the student has taken and done well in. Students will work with the course's instructor on a variety of course-related topics, including preparation of material and development of assignments. Whether CSCI 0082 or its full-credit version ( CSCI 0081 ) is taken is up to the professor of the course being TA'd. Instructor permission required.

CSCI 0100. Data Fluency for All .

This course is intended to introduce Brown students to computational techniques that data scientists use to tell stories. Data fluency encompasses both data literacy, the basics of statistics and machine learning, and data communication, which relies heavily on principles of design. Students will gain hands on experience using statistical tools such as 'R' to analyze real world data sets, and 'ggplot' to visualize them. Sample application domains include just about every field, since the only requirement is data, which there almost always are (e.g., the complete works of Shakespeare is a sample data set).

CSCI 0111. Computing Foundations: Data .

An introduction to computing and programming that focuses on understanding and manipulating data. Students will learn to write programs to process both tabular and structured data, to assess programs both experimentally and theoretically, to apply basic data science concepts, and to discuss big ideas around the communication, use, and social impacts of digital information. Designed for both concentrators and non-concentrators, this is the first course in either a two- or three-course introductory sequence leading into advanced CS courses. Programming assignments will be smaller scale than in CSCI 0150 / 0170 , thus allowing students time to practice programming and discuss computational ideas in a broader context.

CSCI 0112. Computing Foundations: Program Organization .

Explores how organization of programs, data, and algorithms affects metrics such as time performance, space usage, social impacts, and data privacy. Students will learn how to choose between candidate data structures for a problem, how to write programs over several standard data structures, how to assess the quality of programs (from theoretical, practical, and social perspectives), and how to apply their skills to computational problems that could arise in a variety of fields. The course will teach object-oriented programming, in combination with basic functional and imperative programming concepts. The course is designed for both concentrators and non-concentrators. Prerequisite: CSCI 0111

CSCI 0130. User Interfaces and User Experience .

Have you ever had trouble using someone else’s microwave? Have you ever wondered why keyboards are ordered “qwertyuiop”? We will focus on hands-on experience to learn when to use different interfaces, how to model and represent user interaction, how to elicit requirements and feedback from users, as well as the principles of user experience design, methods for designing and prototyping interfaces, and user interface evaluation. Students interested in gaining hands-on experience designing a user interface as well as learning the process behind building an effective interface should take this course. There will be assignments, readings, and workshop time, where students will have the opportunity to work alongside each other as they learn critical tools for interface and web design. This course is open to students that have not taken CSCI 1300 or CSCI 0130 in the past.

CSCI 0150. Introduction to Object-Oriented Programming and Computer Science .

Introduces programming in Java (a modern, widely-used programming language), interactive 2D computer graphics, and some fundamental data structures and algorithms. Students learn by programming a sequence of interactive graphics programs which gradually increase in complexity, including Doodle Jump, Tetris (http://bastilleweb.techhouse.org/), and a significant final project. Lectures are supplemented by skits performed by the UTAs (Undergraduate Teaching Assistants) to teach course concepts and for a bit of added entertainment! This course is intended for both potential concentrators and those who may take only a single course. There are NO prerequisites, and no prior knowledge of programming is required, though students who do have prior programming experience are also encouraged to take the course!

CSCI 0160. Introduction to Algorithms and Data Structures .

Introduces fundamental techniques for problem solving by computer that are relevant to most areas of computer science, both theoretical and applied. Algorithms and data structures for sorting, searching, graph problems, and geometric problems are covered. Programming assignments conform with the object-oriented methodology introduced in CSCI 0150 . Prerequisite: CSCI 0150 or written permission.

CSCI 0170. Computer Science: An Integrated Introduction .

CSCI0170/0180 is an introductory sequence that helps students begin to develop the skills, knowledge, and confidence to solve computational problems elegantly, correctly, efficiently, and with ease. The sequence is unique in teaching both the functional and imperative programming paradigms---the first through the languages Scheme and ML in CSCI0170; the second through Java in CSCI0180. The sequence requires no previous programming experience. Indeed, few high school students are exposed to functional programming; hence even students with previous programming experience often find this sequence an invaluable part of their education. Although students are taught to use programming languages as tools, the goal of CSCI0170/0180 is not merely to teach programming. On the contrary, the goal is to convey to students that computer science is much more than programming! All of the following fundamental computer science techniques are integrated into the course material: algorithms, data structures, analysis, problem solving, abstract reasoning, and collaboration. Concrete examples are drawn from different subareas of computer science: in 0170, from arbitrary-precision arithmetic, natural language processing, databases, and strategic games; in 0180, from discrete-event simulation, data compression, and client/server architectures.

CSCI 0180. Computer Science: An Integrated Introduction .

A continuation of CSCI 0170 . Students learn to program in Java while continuing to develop their algorithmic and analytic skills. Emphasis is placed on object-oriented design, imperative programming, and the implementation and use of data structures. Examples are drawn from such areas as databases, strategy games, web programming, graphical user interfaces, route finding, and data compression. Lab work done with the assistance of TAs. Prerequisite: CSCI 0112 , 0170 , or CSCI 0190 . CSCI 0111 can be used if additional work is done and with the instructor's permission.

CSCI 0190. Accelerated Introduction to Computer Science .

A one-semester introduction to CS covering programming integrated with core data structures, algorithms, and analysis techniques, similar to the two-course introductory sequences ( CSCI 0150 - 0200 and CSCI 0170 - 0200 ). All students wishing to take CSCI 0190 , irrespective of prior preparation, must pass a sequence of online placement assignments during the summer. Though the placement process is most appropriate for students who have had some prior programming experience, it is self-contained so all are welcome to try learning the provided material and attempting placement. Placement information will be available by June 1st at http://cs.brown.edu/courses/csci0190/. Please do not request override codes. The only way to get into the class is through placement. Students who do not successfully pass the placement process won't be allowed to register.

CSCI 0200. Program Design with Data Structures and Algorithms .

Students extend their program-design skills while learning multiple data structures, common graph algorithms, different forms of societal impacts from programs, how to analyze programs for performance, and how to work effectively with multiple styles of programming languages. Examples and course projects draw from several areas of computer science to help students identify their broader interests within the field. There will be a required weekly lab session involving hands-on work with course material. Prerequisite: CSCI 0112 , CSCI 0150 , 0170 , or CSCI 0190 . In addition, CSCI 0111 can be used with both additional work and the instructor's permission. The first two weeks of the course will be taught as at least two parallel tracks based on which prerequisite course a student has taken. CSCI 0200 will be offered every semester (fall and spring).

CSCI 0220. Introduction to Discrete Structures and Probability .

Seeks to place on solid foundations the most common structures of computer science, to illustrate proof techniques, to provide the background for an introductory course in computational theory, and to introduce basic concepts of probability theory. Introduces Boolean algebras, logic, set theory, elements of algebraic structures, graph theory, combinatorics, and probability. No prerequisites.

CSCI 0300. Fundamentals of Computer Systems .

Covers fundamental concepts, principles, and abstractions that underlie the design and engineering of computer systems. Students will learn how a computer works, how to write safe and performant systems software, and what systems abstractions support today’s complex, high-performance systems developed in industry. Specific topics include machine organization, systems programming and performance, key concepts of operating systems, isolation, security, virtualization, concurrent programming, and the basics of distributed systems. Combined lectures, labs, and several hands-on projects involving programming exercises in C/C++. Prerequisites: CSCI 0160 , 0180 , 0190 , or 0200 ; or permission of the instructor.

CSCI 0310. Introduction to Computer Systems .

Basic principles of computer organization. Begins with machine representation of data types and logic design, then explores architecture and operations of computer systems, including I/O, pipelining, and memory hierarchies. Uses assembly language as an intermediate abstraction to study introductory operating system and compiler concepts. Prerequisite: CSCI 0150 or CSCI 0180 or CSCI 0190 .

CSCI 0320. Introduction to Software Engineering .

Focuses on designing, building, testing, and maintaining systems collaboratively. It covers programming techniques (using Java and TypeScript with various frameworks), object-oriented design, advanced testing (e.g., fuzz testing), debugging approaches, and tools such as source control systems. The course concludes with a major group project that students gather requirements for, then design and implement themselves. Prerequisite: CSCI 0160 , 0180 , CSCI 0190 or CSCI 0200 ; CSCI 0220 is recommended.

CSCI 0330. Introduction to Computer Systems .

High-level computer architecture and systems programming. The course covers the organization of computer systems (in terms of storage units, caches, processors, and I/O controllers) and teaches students assembly-language programming and C-language programming. Extensive programming exercises introduce students to systems-level programming on Unix systems, as well as to multi-threaded programming with POSIX threads. Students will be introduced to the functions of operating systems. Prerequisite: CSCI 0160 , 0180 , 0190 , or 0200 .

CSCI 0530. Coding the Matrix: An Introduction to Linear Algebra for Computer Science .

Introduces vectors, matrices and their role in computer science in three components: (1) concepts, theorems, and proofs, (2) procedures and programs, (3) applications and working with data. Weekly lab sessions where students apply concepts to a real task with real data. Example labs: transformations in 2-d graphics, error-correcting codes, image compression using wavelets, synthesizing a new perspective in a photo, face recognition, news story categorization, cancer diagnosis using machine learning, matching airplanes to destinations, Google's PageRank method. Other topics as time allows. Skills in programming and prior exposure to reading and writing mathematical proofs required.

CSCI 0535. Linear Algebra for Machine Learning .

The goal of this course is to provide firm foundations in linear algebra and optimization techniques that will enable students to analyze and solve problems arising in various areas of data science, especially machine learning and data analysis. The students will acquire a firm theoretical knowledge of these concepts and tools. You will also learn how to use these tools in practice by tackling various judiciously chosen projects (from Machine Learning, etc.).

CSCI 1010. Theory of Computation .

The course introduces basic models of computation including languages, finite-state automata and Turing machines. Proves fundamental limits on computation (incomputability, the halting problem). Provides the tools to compare the hardness of computational problems (reductions). Introduces computational complexity classes (P, NP, PSPACE and others). Prerequisite: CSCI0220 or CSCI1450 or CSCI1550 or APMA1650/1655 or CSCI1570

CSCI 1040. The Basics of Cryptographic Systems .

This course will cover cryptographic concepts such as data privacy, encryption, authentication, digital signatures, differential privacy, privacy-enhancing technologies, secure computation, and electronic money. The emphasis will be on how to use cryptographic systems correctly in a larger context, rather than on the mathematical details of how they work; although we will cover some of those details too, on a high level. This course will be aimed at practicing and aspiring poets, economists, software engineers, law and policy wonks, and business tycoons. No prerequisites.

CSCI 1230. Introduction to Computer Graphics .

Fundamental concepts in 2D and 3D computer graphics, e.g., 2D raster graphics techniques and simple image processing. Focuses on geometric transformations, and 3D modeling, viewing and rendering. A sequence of assignments in C++ culminates in a simple geometric modeler and ray tracer. Prerequisite: CSCI 0160 , CSCI 0180 , CSCI 0190 , or CSCI 0200 . Some knowledge of basic linear algebra is helpful but not required. Strong object-oriented programming ability (e.g., in C++, Java or Python) is required.

CSCI 1234. Computer Graphics Lab .

CSCI 1234 is a half-credit course intended to be taken concurrently with CSCI 1230 and provides students with a greater understanding of the material by having them extend each of 1230 's assignments to greater depth.

CSCI 1250. Introduction to Computer Animation .

Introduction to 3D computer animation production including story writing, production planning, modeling, shading, animation, lighting, and compositing. The first part of the course leads students through progressive exercises that build on each other to learn basic skills in 2D and 3D animation. At each step, student work is evaluated for expressiveness, technical correctness and aesthetic qualities. Students then work in groups creating a polished short animation. Emphasis on in-class critique of ongoing work which is essential to the cycle of visually evaluating work in progress, determining improvements, and implementing them for further evaluation. Please see course website for application procedure.

CSCI 1260. Compilers and Program Analysis .

Lexical analysis, syntactic analysis, semantic analysis, code generation, code optimization, translator writing systems. Prerequisites: CSCI 0220 , or CSCI 0320 , or CSCI 0300 , or CSCI 0330 , or CSCI 1310 , or CSCI 1330 .

CSCI 1270. Database Management Systems .

Introduction to database systems internals, design and implementation. Includes data models and structures, languages, query processing and optimization, concurrency control and recovery algorithms. Coverage of relational distributed and parallel databases as well as noSQL big data systems. Prerequisites: One of CSCI 0300 , 0330 (or equivalent coursework).

CSCI 1280. Intermediate 3D Computer Animation .

Continues work begun in CSCI 1250 with deeper exploration of technical and artistic aspects of 3D computer animation including more sophisticated shading and lighting methods and character modeling, rigging, animation, and dynamics. After a series of individual exercises, students pursue an independent topic and then, working alone or in pairs, create a polished demonstration. Emphasis is on in-class critique of ongoing work. Prerequisite: CSCI 1250 . Students may contact the instructor in December for permission.

CSCI 1290. Computational Photography .

Describes the convergence of computer graphics and computer vision with photography. Its goal is to overcome the limitations of traditional photography using computational techniques to enhance the way we capture, manipulate, and interact with visual media. Topics covered: cameras, human visual perception, image processing and manipulation, image based lighting and rendering, high dynamic range, single view reconstruction, photo quality assessment, non photorealistic rendering, the use of Internet-scale data, and more. Students are encouraged to capture and process their own data. Prerequisites: previous programming experience, calculus, and probability; previous knowledge of computer graphics or computer vision. Any full intro sequence and linear algebra are required. Strongly recommended: CSCI 1230 , CSCI 1430 , CSCI 1470 , ENGN 1610 .

CSCI 1300. User Interfaces and User Experience .

Have you ever walked into a door thinking that you were supposed to pull instead of push? Have you ever been stuck on a website, not sure how to proceed next? Learn when to use different interfaces, how to model and represent user interaction, how to elicit requirements and feedback from users, as well as the principles of user experience design, methods for designing and prototyping interfaces, and user interface evaluation. Students interested in both learning the process behind building an effective interface and gaining hands-on experience designing a user interface should take this course. There will be assignments, readings, and studios, where students will have the opportunity to work alongside TAs and interact with industry guests as they learn critical tools for interface and web design. Website: http://cs.brown.edu/courses/csci1300/

CSCI 1310. Fundamentals of Computer Systems .

Covers fundamental concepts, principles, and abstractions that underlie the design and engineering of computer systems, with reference to applications of these concepts in industry. Topics include machine organization, systems programming and performance, key concepts of operating systems, isolation, security, virtualization, concurrent programming, and the basics of distributed systems. Combined lectures, case studies, labs, and several hands-on projects involving programming exercises. This course is intended for Computer Science Master's students only. Anyone else wanting to take the course should contact the instructor.

CSCI 1320. Creating Modern Web & Mobile Applications .

This course covers all aspects of web application development, including initial concept, user-centric design, development methodologies, front and back end development, databases, security, testing, load testing, accessibility, and deployment. There will be a substantial team project. The course is designed for students with a programming background (equiv CSCI 0320 / CSCI 0330 / CSCI 0300 ) who want to learn how to build web applications, and for students with a background in web design, including HTML and Javascript, who are interested in learning how to extend design techniques to incorporate the technologies needed in modern web applications. Project teams will consist of students with both backgrounds.

CSCI 1330. Computer Systems .

High-level computer architecture and systems programming. The course covers the organization of computer systems (in terms of storage units, caches, processors, and I/O controllers) and teaches students assembly-language programming and C-language programming. Extensive programming exercises introduce students to systems-level programming on Linux systems, as well as to multi-threaded programming with POSIX threads. Students will be introduced to the functions of operating systems. Enrollment limited to Master's students only.

CSCI 1340. Introduction to Software Engineering .

CSCI 1340 focuses on designing, building, testing, and maintaining systems collaboratively. It covers programming techniques (using Java and TypeScript with various frameworks), object-oriented design, advanced testing (e.g., fuzz testing), debugging approaches, and tools such as source control systems. The course concludes with a major group project that students gather requirements for, then design and implement themselves. Note: CSCI 1340 is for Master’s students only (they may not register for CSCI 0320 ). It is identical to 0320 but with the addition of supplemental work for each sprint. Prerequisite: CSCI 0160 , CSCI 0180 , CSCI 0190 or CSCI 0200 ; CSCI 0220 is recommended.

CSCI 1360. Human Factors in Cybersecurity .

This course is designed to push you to think about cybersecurity as an idea with both physical and virtual elements. Throughout the course, we will examine the value of information, the importance of users, and the difficult balance between security and usability. The ultimate goal of this course is to give you the intellectual and scientific framework you need to create systems that are both secure and efficient to use. The course focuses on usable security practices, but also looks deeply at the way our society influences security.

CSCI 1370. Virtual Reality Design for Science .

Explores the visual and human-computer interaction design process for scientific applications in Brown's immersive virtual reality Cave. Joint with RISD. Computer Science and design students learn how to work together effectively; study the process of design; learn about scientific problems; create designs applications; critique, evaluate, realize and iterate designs; and demonstrate final projects. Instructor permission required.

CSCI 1380. Distributed Computer Systems .

Explores the fundamental principles and practice underlying networked information systems, first we cover basic distributed computing mechanisms (e.g., naming, replication, security, etc.) and enabling middleware technologies. We then discuss how these mechanisms and technologies fit together to realize distributed databases and file systems, web-based and mobile information systems. Prerequisite: CSCI 0300 , CSCI 0320 , CSCI 0330 , CSCI 1310 or CSCI 1330 .

CSCI 1410. Artificial Intelligence .

Algorithms and representations used in artificial intelligence. Introduction and implementation of algorithms for search, planning, perception, knowledge representation, logic, probabilistic representation and reasoning, robotics and machine learning.

CSCI 1420. Machine Learning .

How can artificial systems learn from examples and discover information buried in data? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised learning. Specific topics include empirical risk minimization, probably approximately correct learning, kernel methods, neural networks, maximum likelihood estimation, the expectation maximization algorithm, and principal component analysis. This course also aims to expose students to relevant ethical and societal considerations related to machine learning that may arise in practice. Please contact the instructor for information about the waitlist.

CSCI 1430. Computer Vision .

How can we program computers to understand the visual world? This course treats vision as inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. Topics may include perception of 3D scene structure from stereo, motion, and shading; segmentation and grouping; texture analysis; learning, object recognition; tracking and motion estimation. Strongly recommended: basic linear algebra, calculus, and probability.

CSCI 1440. Algorithmic Game Theory .

This course examines topics in game theory and mechanism design from a computer scientist's perspective. Through the lens of computation, the focus is the design and analysis of systems utilized by self-interested agents. Students will investigate how the potential for strategic agent behavior can/should influence system design, and the ramifications of conflicts of interest between system designers and participating agents. Emphasis on computational tractability is paramount, so that simple designs are often preferred to optimal. Students will learn to analyze competing designs using the tools of theoretical computer science, and empirical tools, such as empirical game-theoretic analysis. Application areas include computational advertising, wireless spectrum, and prediction markets.

CSCI 1450. Advanced Introduction to Probability for Computing and Data Science .

Probability and statistics have become indispensable tools in computer science. Probabilistic methods and statistical reasoning play major roles in machine learning, cryptography, network security, communication protocols, web search engines, robotics, program verification, and more. This course introduces the basic concepts of probability and statistics, focusing on topics that are most useful in computer science applications. Topics include: modeling and solution in sample space, random variables, simple random processes and their probability distributions, Markov processes, limit theorems, and basic elements of Bayesian and frequentist statistical inference. Basic programming experience required for optional homework assignments. Students cannot get concentration credit for both CSCI 1450 and APMA 1650 / APMA 1655

CSCI 1460. Computational Linguistics .

The application of computational methods to problems in natural-language processing. In particular we examine techniques due to recent advances in deep learning: word embeddings, recurrent neural networks (e.g., LSTMs), sequence-to-sequence models, and generative adversarial networks (GANs). Programming projects include parsing, machine translation, question answering, and chat-bots.

CSCI 1470. Deep Learning .

What is deep learning? How is it related to machine learning? How is it applied to perform tasks like classifying images or translating languages? Deep Learning belongs to a broader family of machine learning methods. Deep learning-based methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders) have led to rapid improvements in applications like computer vision, natural language processing, robotics, and even genomics and health. In this course, you will get an overview of the prominent techniques of deep learning and their applications. This course is designed to help you understand the underlying concepts as well as the promise and pitfalls of deep learning. It also aims at providing hands-on practice of implementing and applying deep learning methods in Python.

CSCI 1480. Building Intelligent Robots .

How do robots function autonomously in dynamic, unpredictable environments? This course focuses on programming mobile robots, such as the iRobot Roomba, to perceive and act autonomously in real-world environments. The major paradigms for autonomous control and robot perception are examined and compared with robotic notions in science fiction. Prerequisite: CSCI 0150 , CSCI 0170 or CSCI 0190 . Recommended: CSCI 1410 or CSCI 1230 .

CSCI 1490. Introduction to Combinatorial Optimization .

This course covers the algorithmic aspects of optimizing decisions in fully observable, non-changing environments. Students are introduced to state-of-the-art optimization methods such as linear programming, integer programming, local search, and constraint programming. Strongly recommended: CSCI 0160 , CSCI 0180 or CSCI 0190 ; CSCI 0510; and CSCI 0530 or MATH 0520 or MATH 0540 .

CSCI 1510. Introduction to Cryptography and Computer Security .

This course studies the tools for guaranteeing safe communication and computation in an adversarial setting. We develop notions of security and give provably secure constructions for such cryptographic objects as cryptosystems, signature schemes and pseudorandom generators. We also review the principles for secure system design. Prerequisites: CSCI 0220 , and either CSCI 0510 or CSCI 1010 .

CSCI 1515. Applied Cryptography .

This course teaches cryptography from a practical perspective and provides hands-on experience of building secure systems in C/C++. Students will implement secure authentication and communication systems using foundational cryptographic algorithms such as encryption schemes, authentication codes, digital signatures, key exchange, and hash functions. The course also covers advanced topics including zero-knowledge proofs, secure multi-party computation, fully homomorphic encryption, and post-quantum cryptography. Students will use these tools to develop applications such as secure online anonymous voting, privacy-preserving data analysis, and private information retrieval.

CSCI 1550. Probabilistic Methods in Computer Science .

Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communications networks and secure protocols. This course introduces the most fundamental probabilistic techniques used in computer science applications, in particular in randomized algorithms, probabilistic analysis of algorithms and machine learning. Prerequisite: Basic background in probability theory course such as CSCI 1450 .

CSCI 1570. Design and Analysis of Algorithms .

A single algorithmic improvement can have a greater impact on our ability to solve a problem than ten years of incremental improvements in CPU speed. We study techniques for designing and analyzing algorithms. Typical problem areas addressed include hashing, searching, dynamic programming, graph algorithms, network flow, and optimization algorithms including linear programming. Prerequisites: CSCI 0160 , CSCI 0180 , or CSCI 0190 , and one of CSCI 0220 , CSCI 1010 , CSCI 1450 , MATH 0750 , MATH 1010 , MATH 1530 .

CSCI 1575. Algorithms: in Depth .

Half-credit course intended to be taken with CSCI 1570 . Students will explore each topic in greater depth by collaboratively solving homework problems that will reinforce valuable new perspectives on the material. Corequisite: CSCI 1570 .

CSCI 1580. Information Retrieval and Web Search .

Covers traditional material as well as recent advances in information retrieval (IR), the study of indexing, processing, and querying of textual data. The focus will be on newer techniques geared to hypertext documents available on the World Wide Web. Topics include efficient text indexing; Boolean and vector space retrieval models; evaluation and interface issues; Web crawling, link-based algorithms, and Web metadata; text/Web clustering, classification; text mining.

CSCI 1590. Introduction to Computational Complexity .

Introduction to serial and parallel models of computation; time and space complexity classes on these models; the circuit model of computation and its relation to serial and parallel time complexity; space-time tradeoffs on serial computers; area-time tradeoffs on the VLSI computational model; interactive and probabilistically checkable proofs; the definition of NP in terms of probabilistically checkable proofs; hardness of approximations to solutions to NP-hard problems. Prerequisite: CSCI 0510.

CSCI 1600. Real-Time and Embedded Software .

Comprehensive introduction to the design and implementation of software for programmable embedded computing systems, in applications such as Internet of Things, transportation, and mobile. Includes the overall embedded real-time software design and development processes, with a focus on engineering for reliability. Major project component. Prerequisites: one of CSCI 0300 , CSCI 0320 , CSCI 0330 , CSCI 1310 , or CSCI 1330

CSCI 1610. Building High-Performance Servers .

In depth study of modern server design. Considers architectures for building high-performance, robust, scalable, and secure network servers. We will consider all aspects of "mission-critical" servers. Topics include multithreaded and asynchronous programming techniques, database access, performance profiling, security, and redundancy. Teams will build significant projects. Prerequisite: CSCI 0320 or 0360. CSCI 1670 or 1680 is recommended.

CSCI 1620. Computer Systems Security Lab .

This course is a half-credit lab intended to be taken concurrently with CS1660 and provides students with a deeper understanding of the material by doing advanced versions of the cs1660’s projects. These advanced versions focus on real-world skills: performing attacks that are more difficult and rely on less serious vulnerabilities, performing attacks against systems with more real-world constraints, and creating attacks that achieve a higher standard of quality than a mere “proof of concept.” Instructor permission required.

CSCI 1650. Software Security and Exploitation .

CSCI 1650 covers software exploitation techniques and state-of-the-art mechanisms for hardening software. The course begins with a summary of prevalent software defects, typically found in applications written in memory unsafe languages, like C/C++, and proceeds with studying traditional and modern exploitation techniques, ranging from classical code injection and code reuse up to the latest goodies (e.g., JIT-ROP). For the most part, it focuses on defenses against certain vulnerability classes and the way(s) to bypass them. Students will be introduced to advanced software exploitation techniques and countermeasures, and study (in depth) the boundaries and effectiveness of standard hardening mechanisms, such as address space randomization and stack and heap protections.

CSCI 1660. Introduction to Computer Systems Security .

This course teaches principles of computer security from an applied viewpoint and provides hands-on experience on security threats and countermeasures. Topics include code execution vulnerabilities (buffer overflow, sandboxing, mobile code), malware (trojans, viruses, and worms), access control (users, roles, policies), cryptosystems (hashing, signatures, certificates), network security (firewalls, TLS, intrusion detection, VPN), and human and social issues. Prerequisites: one of ( CSCI 0160 , 0180 , or 0190 ) and ( CSCI 0300 , 0330 , 1310 , or 1330 ). To be added to the course waitlist, please fill out this form: https://forms.gle/pHPAy9ntQkAQ7xLD9

CSCI 1670. Operating Systems .

Covers not just the principles of operating systems but the intricacies of how they work. Topics include multithreaded programming, managing threads and interrupts, managing storage, processor scheduling, operating-system structure, virtualization, security, and the design of file systems (both local and distributed). Extensive examples are taken from actual systems, including Linux and Windows. Students are expected to complete both problem sets and programming assignments (in C). Prerequisite: CSCI 0300 , 0330 , 1310 , or 1330 .

CSCI 1680. Computer Networks .

Covers the technologies supporting the Internet, from Ethernet and WiFi through the routing protocols that govern the flow of traffic and the web technologies that are generating most of it. A major concern is understanding the protocols used on the Internet: what the issues are, how they work, their shortcomings, and what improvements are on the horizon. Prerequisite: CSCI 0300 , 0330 , 1310 , 1330 or consent of instructor.

CSCI 1690. Operating Systems Laboratory .

Half-credit course intended to be taken with CSCI 1670 . Students individually write a simple operating system in C. Serves to reinforce the concepts learned in 1670 and provides valuable experience in systems programming. Corequisite: CSCI 1670 .

CSCI 1710. Logic for Systems .

The course will focus on proving properties about systems and programs. We will study the distinction between programs and specifications, and check for whether the former obey the latter. We will work with tools that have extensive automation such as model constructors, model checkers, and proof assistants. Problems and projects will apply to real-world systems. Prerequisite: CSCI 0160 , CSCI 0180 , CSCI 0190 , or CSCI 0200 . Preferred but not required: CSCI 0220

CSCI 1729. Programming Languages Lab .

Half-credit course intended to be taken concurrently with CSCI 1730 . Students individually implement a full programming language chosen by the course. Reinforces the concepts learned in CSCI 1730 and provides valuable experience in implementing programming languages. Corequisite: CSCI 1730

CSCI 1730. Design and Implementation of Programming Languages .

Explores the design principles of modern programming languages through implementation, comparison, and reflection. Examines a variety of linguistic features that impact both control and data. Topics vary by year; more information on the course home page. Prerequisite: CSCI 0160 , CSCI 0180 or CSCI 0190 .

CSCI 1760. Multiprocessor Synchronization .

This course examines the theory and practice of multiprocessor synchronization. Subjects covered include multiprocessor architecture, mutual exclusion, wait-free and lock-free synchronization, spin locks, monitors, load balancing, concurrent data structures, and transactional synchronization. Prerequisites: CSCI 0330

CSCI 1780. Parallel and Distributed Programming .

Covers the practical aspects involved in designing, writing, tuning, and debugging software designed to run on parallel and distributed systems. Topics might include client-server computation, threads, networks of workstations, message passing, shared memory, partitioning strategies, load balancing, algorithms, remote procedure call, and synchronization techniques. Prerequisites: CSCI 0220 and either 0320 or 0360; 0510 recommended.

CSCI 1800. Cybersecurity and International Relations .

The global Internet shortens distances, makes businesses more efficient and facilitates greater social interaction. At the same time, it exposes vital national resources to exploitation and makes it easier for the international criminal element to prey on innocent Internet users. Cybersecurity is concerned with making the Internet a more secure and trustworthy environment. In this course we study this topic from the technological and policy points of view. The goal is to facilitate communication across the divide that normally characterizes the technological and policy communities.

CSCI 1805. Computers, Freedom and Privacy .

Who is the Big Brother that we most fear? Is it the NSA -- or is it Google and Facebook? Rapidly changing social mores and the growing problem of cybersecurity have all contributed to a sense that privacy is dead. Laws protecting privacy and civil liberties are stuck in the analog age, while the capabilities for mass digital surveillance continue to advance rapidly. This course will examine a variety of informational privacy and technology issues. A major theme: the historical and contemporary struggle to bring surveillance under democratic control to protect against abuses of privacy, civil liberties and human rights.

CSCI 1810. Computational Molecular Biology .

High-throughput experimental approaches now allow molecular biologists to make large-scale measurements of DNA, RNA, and protein, the three fundamental molecules of the cell. The resulting datasets are often too large for manual analysis and demand computational techniques. This course introduces algorithms for sequence comparison and alignment; molecular evolution and phylogenetics; DNA/RNA sequencing and assembly; recognition of genes and regulatory elements; and RNA and protein structure. The course demonstrates how to model biological problems in terms of computer science. Prerequisites: CSCI 0160 , CSCI 0180 or CSCI 0190 , or consent of instructor.

CSCI 1820. Algorithmic Foundations of Computational Biology .

The course is devoted to computational and statistical methods as well as software tools for DNA, RNA, and protein sequence analysis. The focus is on understanding the algorithmic and mathematical foundations of the methods, the design of associated genomics software tools, as well as on their applications. Topics include: sequence alignment, genome assembly, gene prediction, regulatory genomics, and SNP's variation. The course is open to computer and mathematical sciences students as well as biological and medical students.

CSCI 1850. Deep Learning in Genomics .

Deep learning models have achieved impressive performance in fields like computer vision and NLP. The collection of vast quantities of biological data naturally leads to the question -- can deep learning help us understand genomics? We will cover deep learning models like Auto-encoders and Convolutional Neural Networks and how have they been applied to solve problems in genomics. We will learn about different biological datasets, interpretation methods that help explain predictions, and what unique challenges are presented by the data in this field. Critical thinking and learning from the practical application of models to data are expected outcomes.

CSCI 1860. Cybersecurity Law and Policy .

Course description: Cybersecurity and cyber conflict pose unique legal and policy challenges for governments, companies and citizens. The way those problems are resolved will shape the future of the internet. This course will examine cybersecurity as a legal and policy problem. How can government and society address network and computer insecurity while upholding privacy, civil liberties and other fundamental values?

CSCI 1870. Cybersecurity Ethics .

This timely, topical course offers a comprehensive examination of ethical questions in cybersecurity. These issues pervade numerous, diverse aspects of the economy and society in the Information Age, from human rights to international trade. Students will learn about these topics, beginning first with acquaintance with the dominant ethical frameworks of the 20th and 21st centuries, then employing these frameworks to understand, analyze, and develop solutions for leading ethical problems in cybersecurity. The things that you learn in this course will stay with you and inform your personal and professional lives.

CSCI 1880. Introduction to Computer Security .

This course examines the basic principles of computer security for an organization, recognizing which system components relate to which principles. Additionally, the course covers methodologies and skills for making informed security decisions and understanding how to apply security principles to design security mechanisms while considering tradeoffs. Topics include general security principles, cryptography, authentication authorization, identity, and access management, operating systems security, network security, web security, and applications security. Throughout the course, you will develop a preliminary cybersecurity plan for an organization

CSCI 1900. csciStartup .

In csciStartup, you will incorporate and run a startup. Apply as a team to be part of a prototype class to remove the mystery from starting a company and to focus entirely on a product you're passionate about. Teams will incorporate, build a product for real customers, advertise their product, and improve it week after week. We'll spend half our class meetings with individual attention to each group's progress and how to improve your offerings. Assignments will be designed to apply to any company, with enough flexibility to ensure you're always working on things that make sense for your business.

CSCI 1950A. Computational Modeling and Algorithmic Thinking .

In this course you will learn how to apply tools from statistics and computer science to build computational models of physical and biological systems. Example applications include modeling and then simulating the behavior of a collection of genes, the spread of disease in a population, a single neuron in isolation or the complex of neurons comprising the primate visual cortex.

CSCI 1950B. Computational Topology and Discrete Geometry .

This course will investigate (through a mixture od lectures and student presentations of recent papers) topics in computational topology, including Morse theory and discrete differential geometry. Other possible topics are knot polyonmials, simplicial homology, and geometric probability theory. Some mathematical sophistication and programming skills required. No prerequisites.

CSCI 1950H. Computational Topology .

We will study various algorithmic problems that arise in the study of topological phenomena, such as winding number, turning number, knot polynomials, topology of covering spaces (especially Riemann surfaces), and discrete Morse theory. The mathematical topics will be briefly introduced before we move to computations, but some a priori mathematical sophistication will make the course more valuable to the student. Prerequisite: CSCI 0160 , 0180 , or 0190 .

CSCI 1950J. Introduction to Computational Geometry .

Geometric algorithms in two and three dimensions. Algorithmic and geometric fundamentals. Point location, convex hulls, proximity (Voronoi diagrams, Delaunay triangulations), intersections, the geometry of rectangles. Prerequisites: CSCI 0160 , 0170 , or 0190 ; and CSCI 0220 .

CSCI 1950K. Innovating Game Development .

A project-centered course focused on technological, paradigm, and design innovations for game development. As teams, students will propose and implement a project demonstrating a novel technology for gaming. Examines the current state and future of game development through a seminar of speakers active in game development and research. A strong computer science or engineering background is recommended.

CSCI 1950M. Advanced Practical Combinatorial Algorithms .

We review recent as well as well-established advanced techniques in combinatorial optimization and constraint satisfaction. Students will study and individually present research papers and work on challenging software projects in small teams. Prerequisites: CSCI 0160 , 0180 , or 0190 ; and CSCI 0510; and CSCI 1490 or 2580 , or instructor permission.

CSCI 1950N. 2D Game Engines .

2D Game Engines covers core techniques used in the development of the software that drives computer games and other interactive software. Projects involve building different varieties of 2D game engines as well as games that require use of the features implemented in the engines. Topics include high-level engine design, vector and raster graphics, animation, collision detection, physics, content management, and game AI. Prerequisite: CSCI 0160 , 0180 , or 0190 . This course has also been offered as DISP CSCI1971. Students interested in an override should request on through Courses@Brown. Priority will be given to both seniors and juniors.

CSCI 1950T. Advanced Animation Production .

Students will apply knowledge and skills gained in previous animation courses to produce a high quality short animated film as a group. Production will follow the industry standard pipeline that includes modeling, texturing, lighting, animating, rendering, and post production. Interested students will perform preproduction story and concept design prior to beginning of course. Prerequisite: CSCI 1250 . Enrollment limited to 15. Instructor permission required.

CSCI 1950U. Topics in 3D Game Engine Development .

Covers core techniques in 3D game development with an emphasis on engine architecture. Students independently develop their own engines using C++, OpenGL, and the Qt framework, then work in groups to create a polished game. Topics include: spatial subdivision, player representation, collision detection and response, game networking, GPUs, and OpenGL. Prerequisites: CSCI 1230 or knowledge of C++ and one of CSCI0300, CSCI0320, or CSCI0330 or equivalent experience. Enrollment limited to 25.

CSCI 1950X. Software Foundations .

Software Foundations will be a project-based course focusing on the challenges and techniques involved in proving non-trivial properties about real-world systems. We will base our exploration around formal development in a proof environment. Roughly half of the course will be a guided tutorial of proof techniques using one or more theorem provers; in the remainder, students will apply this knowledge to existing systems. No prior experience with theorem provers or proof assistants is necessary, but familiarity with and aptitude for functional programming will be a huge bonus. Prerequisite: CSCI 1730 or equivalent; mathematical maturity.

CSCI 1950Z. Computational Methods for Biology .

This course will introduce algorithms from machine learning and combinatorial optimization with a focus on their application to biological data. Topics will include problems in phylogenetic inference, population genetics, and biological interaction networks.

CSCI 1951A. Data Science .

Mastering big data requires skills spanning a variety of disciplines: distributed systems over statistics, machine learning, and a deep understanding of a complex ecosystem of tools and platforms. Data Science refers to the intersection of these skills and how to transform data into actionable knowledge. This course provides an overview of techniques and tools involved and how they work together: SQL and NoSQL solutions for massive data management, basic algorithms for data mining and machine learning, information retrieval techniques, and visualization methods. Prerequisites: CSCI 0160 , CSCI 0180 , CSCI 0190 , or CSCI 0200 . One of CSCI 0300 , 0330 , CSCI 0320 , 1310 or 1330 strongly recommended.

CSCI 1951B. Virtual Citizens or Subjects? The Global Battle Over Governing Your Internet .

The Internet began as a U.S. government research project, progressed to an open network run by free-spirited geeks, and transitioned in the late 1990’s to a unique governance model in which nations, corporations, and civil society were supposed to all have a voice. Where are the real decisions being made? Who is making them? How can you and citizens of other nations influence these decisions? The global battle to run the Internet, brewing for years, has broken wide open with revelations of American spying on a massive scale.

CSCI 1951C. Designing Humanity Centered Technology .

This semester we will explore how emerging technologies might shape our lives in the near future, as we design and build working prototypes. We will proceed from a set of questions that will complement a deep immersion in design process and creative practice. We will explore the “how” and “why” of designing new technologies. The course will help students build a portfolio of design projects that are in response to various design strategies such as Human Centered Design, Speculative Design, Critical Design, and Design Fiction, as well as developing skills for iterative prototyping and participatory critique. Students interested in registering should sign up here: https://docs.google.com/forms/d/e/1FAIpQLSdvo0o4ICpj55ZubZQTXdVRfBmnbCHbT8egriwPOcWcbRiy6A/viewform

CSCI 1951G. Optimization Methods in Finance .

Optimization plays an important role in financial decisions. Many computational finance problems ranging from asset allocation to risk management, from option pricing to model calibration can be solved efficiently using modern optimization techniques. This course discusses several classes of optimization problems (including linear, quadratic, integer, dynamic, stochastic, conic, and robust programming) encountered in financial models. For each problem class, after introducing the relevant theory and efficient solution methods, we discuss problems of mathematical finance that can be modeled within this problem class. Prerequisites: CSCI 1450 or APMA 1650 , and CSCI 1570 .

CSCI 1951I. CS for Social Change .

Working in a studio environment to iteratively design, build, and test technical projects in partnership with different social change organizations, students will be placed in small teams to collaboratively work on projects that will range from developing a chatbot to aid community engagement to conducting geospatial data analytics. We will also reflect on our positionality and ethics in engaging in social impact work and what it practically means to leverage technology to create social change on an everyday basis.

CSCI 1951J. Interdisciplinary Scientific Visualization .

Students will learn about solving scientific problems using computer graphics and visualization. Projects will involve the solution of scientific problems using computer graphics, modeling, and visualization. Working in small groups, students will identify scientific problems, propose solutions involving computational modeling and visualization, evaluate the proposals, design and implement the solutions, apply them to the problems, evaluate their success, and report on results. Example projects might include interactive software systems, immersive virtual reality cave applications, quantitative analysis tools, or new applications of existing visualizations methods. The focus will be on applications in the new virtual reality cave.

CSCI 1951L. Blockchains and Cryptocurrencies .

Introduction to modern blockchain-based systems. Topics covered include consensus and distributed computing, examples cryptocurrencies, programming smart contracts, privacy and secrecy, transfer networks, atomic swaps and transactions, non-currency applications of blockchains, and legal and social implications. Students will do a programming project and a term project.

CSCI 1951N. VR+X, The Potential of Virtual Reality to Transform Nearly Everything .

This course introduces students to the history, present, and future possibilities of virtual reality (VR) with a focus on addressing the question: What is the transformative potential of virtual reality? We’ll critically evaluate a variety of applications in fields as varied as healthcare, architecture, education, and storytelling. Students will learn discovery and design thinking processes of a kind that can lead to the development of VR solutions. Students will create a design concept for a VR use case in a field of their choosing.

CSCI 1951P. Design of Robotic Systems (ENGN 1931I) .

Interested students must register for ENGN 1931I .

CSCI 1951R. Introduction to Robotics .

Each student will learn to program a small quad-rotor helicopter. We will provide each student with their own robot for the duration of the course. The course will cover PID controllers for stable flight, localization with a camera, mapping, and autonomous planning. At the end of the course, the aim is for students to understand the basic concepts of a mobile robot and aerial vehicle. Enrollment by instructor permission.

CSCI 1951T. Surveying VR Data Visualization Software for Research .

In a collaborative group effort, this course will search out, install, test, and critically evaluate VR software that supports data visualization for researchers. We will target several specific types of data, including volumetric data, and remote sensing data. We will investigate the capabilities of software for head-mounted displays (HMDs), big-metal displays like caves and the yurt, and, as a baseline, desktop displays. Software evaluation will include web research, hands-on case studies, and surveying. Results will be documented in a courses wiki.

CSCI 1951V. Hypertext/Hypermedia: The Web Was Not the Beginning and the Web Is Not the End .

Hypertext/Hypermedia systems -- first designed in the 1960s -- link information and people. Developed in the late 1980s, the Web was the first global hypermedia system; 30+ years later, it represents a small part of past visions. Students will identify still-uncommon features by exploring/using systems from the 1960s onwards. They will read papers for class discussion. They will study architecture and design topics such as annotating, note taking, searching, networking, collaboration, permanence, and social impact. Web programming projects, using TypeScript/MERN stack, will culminate in group projects to create their own hypertext/hypermedia systems. Prerequisites: An introductory CS sequence or equivalent experience

CSCI 1951W. Sublinear Algorithms for Big Data .

A huge quantity of data is worth little unless we can extract insights from it. Yet, the large quantities mean that classic algorithms (running in linear, quadratic or even more time) can be infeasible in practice. We must instead turn to new algorithmic approaches and paradigms, which allow us to answer valuable questions about our data in runtime that is still feasible even when the data set is Facebook-sized. Surprisingly, to answer many computational and statistical questions, sometimes there is no need to read/store every piece of data! This course focuses on this exciting "sublinear" algorithmic regime. We will study practical algorithms, making clever use of randomness with strong theoretical guarantees Prerequisites: (CS22 or equivalent); (CS145 or APMA1650/1655 or equivalent); (CS157 or CS155). Mathematical maturity is essential: this is a theory course with proofs. Recommended: CS155

CSCI 1951X. Formal Proof and Verification .

Proof assistants are tools that are used to check the correctness of programs. Unlike tools like model checkers and SAT solvers, proof assistants are highly interactive. Machine-checked formal proofs lead to trustworthy programs and fully specified reliable mathematics. This course introduces students to the theory and use of proof assistants, using the system Lean. We will use Lean to verify properties of functional programs and theorems from pure mathematics. We will learn the theory of deductive reasoning and the logic that these tools are based on. Text: "The Hitchhiker's Guide to Logical Verification" by Blanchette et al. Prereqs: CSCI 1710 Logic for Systems or a proof-based mathematics course. Basic familiarity with functional programming (e.g. Haskell, ML) is helpful but not required.

CSCI 1951Z. Fairness in Automated Decision Making .

We know we want to build more equitable technology, but how? In this course we’ll review the latest developments in how to build more equitable algorithms, including definitions of (un)fairness, the challenges of explaining how ML works, making sure we can get accountability, and much more.

CSCI 1952B. Responsible Computer Science in Practice .

What can ethics and social and political theory tell us about how to navigate the social impacts of computing? How do these perspectives shape technical decisions computer scientists have to make? The role of computer scientists is rapidly evolving: as the systems they build affect everyone, from individuals to society at large, computer scientists become more than just coders. They must be able to assess the social impacts of the technology they develop and engage with experts from other disciplines which offer critical insights and normative perspectives on those impacts. The goal of this course is to enable you to understand and critically reflect on key concepts and ideas in ethics and social and political theory on topics ranging from fairness to consent, digital well-being to regulation, and to apply them to concrete technical decisions in practical exercises and project-oriented work.

CSCI 1952I. Language Processing in Humans and Machines (CLPS 1850) .

Interested students must register for CLPS 1850 .

CSCI 1952Q. Algorithmic Aspects of Machine Learning .

In this course, we will explore the theoretical foundations of machine learning and deep learning. We will focus on designing and analyzing machine learning algorithms with provable guarantees. More specifically, in this course we will (1) introduce basic tools in linear algebra and optimization, including the power method, singular value decomposition, matrix calculus, (matrix) concentration inequalities, and (stochastic) gradient descent, (2) cover many examples where one can design algorithms with provably guarantees for fundamental problems in machine learning (under certain assumptions), including topic modeling, tensor decomposition, sparse coding, and matrix completion, and (3) discuss the emerging theory of deep learning, including landscape analysis, generalization and over-parameterization, neural tangent kernels, generalization bounds, and implicit regularization.

CSCI 1952V. Algorithms for the People .

Computer science has transformed every aspect of society, including communication, transportation, commerce, finance, and health. The revolution enabled by computing has been extraordinarily valuable. The largest tech companies generate almost a trillion dollars a year and employ millions of people. But technology does not affect everyone in the same way. In this seminar, we will examine how new technologies, ranging from facial recognition to drones, are affecting marginalized communities.

CSCI 1952X. Contemporary Digital Policy and Politics .

This course will examine the politics and processes for making policies related to the internet and digital policy issues. We will examine current issues at the national level, including the White House and federal agencies, Congress, international institutions and industry on issues such as privacy and information security, and on debates like whether and how to regulate Big Tech. Topics covered include the creation of national policies at the White House, the regulatory process, legislation, standards, global implications and the politics of technological change. Format and participation: This is an asynchronous version of IAPA 1811 , available only to students enrolled in a completely online master’s degree program, by permission of the instructor. Students will complete weekly activities in lieu of attending synchronous class discussions.

CSCI 1970. Individual Independent Study .

Independent study in various branches of Computer Science. Section numbers vary by instructor. Please check Banner for the correct section number and CRN to use when registering for this course.

CSCI 1971. Independent Study in 2D Game Engines .

2D Game Engines covers core techniques used in the development 2D game engines. Projects involve building different varieties of 2D game engines as well as games that require use of the features implemented in the engines. Topics include high-level engine design, vector and raster graphics, animation, collision detection, physics, content management, and game AI. Prerequisite: CSCI 0160 , 0180 , or 0190 .

CSCI 1972. Topics in 3D Game Engine Development .

Covers core techniques in 3D game development with an emphasis on engine architecture. Students independently develop their own engines using C++, OpenGL, and the Qt framework, then work in groups to create a polished game. Topics include: spatial subdivision, player representation, collision detection and response, game networking, GPUs, and OpenGL. Prerequisite: CSCI 1230 and one of the following CSCI 0320 , CSCI 0330 , CSCI 1950N , OR CSCI 1971 .

CSCI 1973. Independent Study .

CSCI 2000. Computer Science Research Methods .

What does it mean to conduct research in computer science, and how might we be most effective at it? To help begin a fruitful career in CS research, this class will cover the philosophy and practice of forming ideas, executing research, presenting outcomes, and understanding and contributing to our community. The aim is to kick-start your time at Brown CS by being the 'missing semester' on how to be a PhD student, and by peeling back the curtain on why CS academia works like this to help you make the most of your time. Discussions include: motivating, pitching, and funding research; finding, reading, and reviewing research; selecting research areas and forming hypotheses; designing, performing, and evaluating research; communicating research; research collaborations; and research ethics. We will learn together through presentations, activities, discussions, plus readings and assignments out of class.

CSCI 2002. Privacy and Personal Data Protection .

If you tried to live for one day without generating any digital personal data, how would you spend it? In the Information Age, the use of personal data has proliferated and is pervasive. This course offers a comprehensive examination of protection of privacy and personal data, which is central to autonomy, dignity, and liberty. Topics include identity, financial, health, educational, and other data. Students will learn about: Fair Information Practices; the development of modern privacy rules in the United States and around the world; Fourth Amendment privacy and the autonomy of the individual in relation to the state; key US laws (HIPAA, FERPA, GLBA, GINA, COPPA, etc.); significant international rules (European Union’s General Data Protection Regulation (GDPR), etc.); important institutions (Federal Trade Commission, Data Protection Authorities, etc.); standards; Privacy by Design and Default; and emerging issues.

CSCI 2230. Computer Graphics .

This course offers an in-depth exploration of fundamental concepts in 2D and 3D computer graphics. It introduces 2D raster graphics techniques, including simple image processing. The bulk of the course is devoted to 3D modeling, geometric transformations, and 3D viewing and rendering. A sequence of assignments culminates in a simple geometric modeler and ray tracer. C++ and the graphics library OpenGL are used throughout the course, as is shader programming on the GPU. The final project is typically a small group project spec'd and implemented by the group using shaders or ray tracing to create special effects.

CSCI 2240. Advanced Computer Graphics .

CSCI 2240 explores several key areas of 3D graphics---rendering, geometry processing, optimization, and simulation---taking a sophisticated approach to each. This year, we are looking to improve the course's coverage of optimization by adding more lecture content on the topic (optimization theory, methods for solving (sparse) linear systems, etc.) and by designing a new assignment (likely 3D as-rigid-as-possible shape manipulation). Prerequisites: one of CSCI 0530 , MATH 0520 , MATH 0540 ; CSCI 1230 ; and familiarity with multivariable calculus by e.g. having taken one of MATH 180, MATH 200, MATH 350

CSCI 2270. Topics in Database Management .

In-depth treatment of advanced issues in database management systems. Topics vary from year to year and may include distributed databases, mobile data management, data stream processing and web-based data management. Prerequisite: CSCI 1270 .

CSCI 2300. Human-­Computer Interaction Seminar .

Covers methods for conducting research in human-computer interaction (HCI). Topics will be pursued through independent reading, assignments, and class discussion. Comprises four assignments that apply to HCI research methods and push the envelope, which are designed to be meaningful and have the potential for real impact. Students will gain the background necessary to perform research in HCI and the skills to conduct human-centric research. There will be little content about user interfaces, but students may find some topics in CSCI 1300 relevant. Please see the course website when it's available (shortly before the semester begins) for information about overrides.

CSCI 2310. Human Factors and User Interface Design .

Covers current research issues involving the implementation, evaluation and design of user interfaces, while also providing a basic background in the fundamentals of user interface evaluation, programming, tools, and techniques. A possible topic is programming and designing device-independent interfaces. Previous topics have included the development of pervasive internet-based interfaces and software visualization. Prerequisite: Consent of instructor.

CSCI 2330. Programming Environments .

Programming tools; control and data integration; software understanding and debugging; environments for parallel and distributed programming; reverse engineering; configuration management and version control and debugging. Emphasis on current research areas. Prerequisite: consent of instructor.

CSCI 2340. Software Engineering .

Topics in the design, specification, construction and validation of programs. Focus will be on tools to support each of these stages. Course will pay special attention to the concerns raised by the properties of modern software systems including distribution, security, component-based decomposition and implicit control. A basic software engineering course such as CSCI0320 or CSCI1340 or extensive industrial programming experience is required. Knowledge of system programming such as CSCI0300,CSCI0330, CSCI1310, or CSCI1330 is highly recommended.

CSCI 2370. Interdisciplinary Scientific Visualization .

Learn how to do research on using computer graphics, visualization, and interaction applied to scientific problems. Working in small multidisciplinary groups, students identify scientific problems, propose solutions involving computational modeling and visualization, design and implement the solutions, apply them to the problems, and evaluate their success. Prerequisites: programming experience, some graphics experience, problem ideas.

CSCI 2390. Privacy-Conscious Computer Systems .

We will examine research papers on distributed system design, privacy-preserving, and secure computing techniques, and discuss how to apply these ideas in practice. The goal is to understand if, and how we can better protect the sensitive data we entrust to computer systems, both against leaks and against unauthorized or unethical use. We will look at web services, datacenter systems, distributed communication systems, and machine learning systems. During class, you will present and discuss papers, finish a set of hands-on assignments, work on a research project, and present your project at the end of the semester.

CSCI 2410. Statistical Models in Natural-Language Understanding .

Various topics in computer understanding of natural language, primarily from a statistical point of view. Topics include: hidden Markov models, word-tagging models, probabilistic context-free grammars, syntactic disambiguation, semantic word clustering, word-sense disambiguation, machine translation and lexical semantics. Prerequisite: CSCI 1410 .

CSCI 2420. Probabilistic Graphical Models .

Probabilistic graphical models provide a flexible framework for modeling large, complex, heterogeneous collections of random variables. After a brief introduction to their representational power, we provide a comprehensive survey of state-of-the-art methods for statistical learning and inference in graphical models. We discuss a range of efficient algorithms for approximate inference, including optimization-based variational methods, and simulation-based Monte Carlo methods. Several approaches to learning from data are explored, including conditional models for discriminative learning, and Bayesian methods for controlling model complexity. Programming experience required for homeworks and projects, which integrate mathematical derivations with algorithm implementations. PREREQUISITES: CSCI1420 or APMA1690.

CSCI 2430. Topics in Machine Learning .

Machine learning from the artificial intelligence perspective, with emphasis on empirical validation of learning algorithms. Different learning problems are considered, including concept learning, clustering, speed-up learning, and behavior learning. For each problem a variety of solutions are investigated, including those from symbolic AI, neural and genetic algorithms, and standard statistical methods. Prerequisite: CSCI 1410 or familiarity with basic logic and probability theory.

CSCI 2440. Advanced Algorithmic Game Theory .

This course examines topics in game theory and mechanism design from a computer scientist’s perspective. Through the lens of computation, the focus is the design and analysis of systems utilized by self-interested agents. Students will investigate how the potential for strategic agent behavior can/should influence system design, and the ramifications of conflicts of interest between system designers and participating agents. Emphasis on computational tractability is paramount, so that simple designs are often preferred to optimal. Students will learn to analyze competing designs using the tools of theoretical computer science, and empirical tools, such as empirical game-theoretic analysis. Application areas include computational advertising, wireless spectrum, and prediction markets.

CSCI 2450. Exchange Scholar Program .

CSCI 2470. Deep Learning .

Deep Learning belongs to a broader family of machine learning methods. It is a particular version of artificial neural networks that emphasizes learning representation with multiple layers of networks. Deep Learning, plus the specialized techniques that it has inspired (e.g. convolutional neural networks, recurrent neural networks, and transformers), have led to rapid improvements in many applications, such as computer vision, machine learning, sound understanding, and robotics. This course gives students an overview of the prominent techniques of Deep Learning and its applications in computer vision, language understanding, and other areas. It also provides hands-on practice of implementing deep learning algorithms in Python. A final project will implement an advanced piece of work in one of these areas. Prerequisites: basic programming: ( CSCI 0150 , 0170 , 0190 ); linear algebra: ( CSCI 0530 , MATH 0520 , 0540 ); stats/probability: ( CSCI 0220 , 1450 , 0450, MATH 1610 , APMA 1650 , 1655 )

CSCI 2500A. Advanced Algorithms .

In this course, we study a selection of advanced algorithms and data structures that are provably correct and fast. Our goal is to present a broad range of algorithmic ideas and techniques, especially those that have had significant impact on the field and/or have had or might have practical impact. Prerequisite: CSCI 1570 or the equivalent

CSCI 2500B. Optimization Algorithms for Planar Graphs .

Planar graphs arise in applications such as road map navigation and logistics, graph drawing and image processing. We will study graph algorithms and data structures that exploit planarity. Our focus will be on recent research results in optimization. Prerequisite: CSCI 1570 or the equivalent.

CSCI 2510. Approximation Algorithms .

Approximation Algorithms deal with NP-hard combinatorial optimization problems by efficiently constructing a suboptimal solution with some specified quality guarantees. We study techniques such as linear programming and semidefinite programming relaxations, and apply them to problems such as facility location, scheduling, bin packing, maximum satifiability or vertex cover. Prerequisite - one of the following: CSCI 1510 , 1550 , 1810 , 1950J , 1950L, any graduate-level course on algorithms (including 2500A , 2500B , 2580 ).

CSCI 2520. Computational Geometry .

Algorithms and data structures for fundamental geometric problems in two and three dimensions. Topics include point location, range searching, convex hull, intersection, Voronoi diagrams, and graph drawing. Applications to computer graphics, circuit layout, information visualization, and computer-aided design are also discussed. Prerequisite: CSCI 1570 or instructor permission.

CSCI 2530. Design and Analysis of Communication Networks .

A theory seminar focusing on algorithmic and combinatorial issues related to the design and analysis of communication networks for parallel and distributed systems. Topics include packet routing, circuit switching, distributed shared memory, fault tolerance, and more. Prerequisites: CSCI 1550 , 1570 , or equivalent.

CSCI 2531. Internet and Web Algorithms .

This advanced graduate course/seminar focuses on the mathematical foundations of algorithms for handling large amounts of data over networks. We'll read and discuss recent papers in information retrieval, search engines, link analysis, probabilistic modeling of the web and social networks, and more. Recommended: CSCI 1550 and CSCI 1570 , or equivalent courses.

CSCI 2540. Advanced Probabilistic Methods in Computer Science .

Advanced topics in applications of probabilistic methods in design and analysis of algorithms, in particular to randomized algorithms and probabilistic analysis of algorithms. Topics include the Markov chains Monte Carlo method, martingales, entropy as a measure for information and randomness, and more. Prerequisite: CSCI 1450 . Recommended but not required: CSCI 1570 .

CSCI 2550. Parallel Computation: Models, Algorithms, Limits .

The theoretical foundations of parallel algorithmics. Analysis of the most important models of parallel computation, such as directed-acyclic computation graphs, shared memory and networks, and standard data-exchange schemes (common address space and message-passing). Algorithmic techniques with numerous examples are cast mostly in the data-parallel framework. Finally, limitations to parallelizability (P-completeness) are analyzed. The content of the course is likely to change as technology evolves.

CSCI 2560. Advanced Complexity .

Advanced topics in computational complexity, such as: the polynomial hierarchy, interactive proofs, pseudorandomness, derandomization, probabilistically checkable proofs.

CSCI 2570. Introduction to Nanocomputing .

Nanoscale technologies employing materials whose smallest dimension is on the order of a few nanometers are expected to replace lithography in the design of chips. We give an introduction to computational nanotechnologies and explore problems presented by their stochastic nature. Nanotechnologies based on the use of DNA and semiconducting materials will be explored. Prerequisite: CSCI 0510.

CSCI 2580. Solving Hard Problems in Combinatorial Optimization: Theory and Systems .

The theory of combinatorial optimization and how it is embodied in practical systems. Explores issues encountered in implementing such systems. Emphasizes the wide variety of techniques and methodologies available, including integer programming, local search, constraint programming, and approximation algorithms. Problems addressed may include: scheduling, coloring, traveling salesman tours, and resource allocation. Prerequisites: CSCI 0320 and basic knowledge of linear algebra.

CSCI 2590. Advanced Topics in Cryptography .

Seminar-style course on advanced topics in cryptography. Example topics are zero-knowledge proofs, multi-party computation, extractors in cryptography, universal composability, anonymous credentials and ecash, interplay of cryptography and game theory. May be repeated for credit. Prerequisite: CSCI 1510 or permission of the instructor.

CSCI 2660. Computer Systems Security .

This course teaches computer security principles from an applied viewpoint and provides hands-on experience with security threats and countermeasures. The course additionally covers principles and skills useful for making informed security decisions and for understanding how security interacts with the world around it. The main topics covered are cryptography, authentication, access control, web security, and network security. Other topics include cybersecurity ethics and privacy. The course aims to balance theory and practice. These advanced versions focus on real-world skills: performing attacks that are more difficult and rely on less serious vulnerabilities, and creating attacks that achieve a higher standard of quality than a mere ”proof of concept.” This course covers the same material as CSCI 1620 and 1660 and shares their assignments. Graduate students only. If you are interested in this course, request an override and fill out this form: https://forms.gle/pHPAy9ntQkAQ7xLD9

CSCI 2670. Operating Systems .

Covers not just the principles of operating systems but the intricacies of how they work. Topics include multithreaded programming, managing threads and interrupts, managing storage, processor scheduling, operating-system structure, virtualization, security, and the design of file systems (both local and distributed). Extensive examples are taken from actual systems, including Linux and Windows. Students are expected to complete both problem sets and programming assignments (in C) and will individually write a simple operating system. Prerequisite: one of CSCI 0300 , CSCI 0330 , CSCI 1310 , or CSCI 1330 . Graduate students only. This course covers the same material as the combination of CSCI 1670 and 1690 and shares their assignments.

CSCI 2730. Programming Language Theory .

Theoretical models for the semantics of programming languages and the verification of programs. Topics will be drawn from operational semantics, denotational semantics, type theory and static analyses. Recommended prerequisite: CSCI 1730 , CSCI 1950Y or instructor permission.

CSCI 2750. Topics in Parallel and Distributed Computing .

CSCI 2750 is a graduate seminar that will consider an advanced topic (to be determined) in distributed computing. May be repeated for credit.

CSCI 2810. Advanced Computational Molecular Biology .

High-throughput experimental approaches now allow molecular biologists to make large-scale measurements of DNA, RNA, and protein, the three fundamental molecules of the cell. The resulting datasets are often too large for manual analysis and demand computational techniques. This course introduces algorithms for sequence comparison and alignment; molecular evolution and phylogenetics; DNA/RNA sequencing and assembly; recognition of genes and regulatory elements; and RNA and protein structure. The course demonstrates how to model biological problems in terms of computer science. CSCI 0160 , 0180 , 0190 , or 0200 . Recommended: CS 220, or some other course that introduces concepts from discrete math and probability. Course overrides are available at the instructor’s discretion.

CSCI 2820. Algorithmic Foundations of Computational Biology .

The aim of this course is to provide computer science and mathematical sciences foundations, as well as biological insights, for numerous seminal algorithms in the field of computational biology, i.e., algorithmic foundations for Computational Biology. Topics include: The BLAST Algorithm and Karlin-Altschul Statistics, Genome Assembly Algorithms and Haplotype Assembly Algorithms, Hidden Markov Models (HMM) Algorithms: The Learning Problem, Recombination and Ancestral Recombination Graphs Algorithms, Rigorous Clustering: Spectral Graph Theory Algorithms, Algorithms for Constructing Suffix Trees in Linear Time, Protein Folding Algorithms (An Introduction). Each chapter is devoted to a class of fundamental computational problems of genomics related to the analysis of DNA, RNA, protein sequences and protein structures and their molecular biology function.

CSCI 2840. Advanced Algorithms in Computational Biology and Medical Bioinformatics .

Devoted to computational problems and methods in the emerging field of Medical Bioinformatics where genomics, computational biology and bioinformatics impact medical research. We will present challenging problems and solutions in three areas: Disease Associations, Protein Folding and Immunogenomics. This course is open to graduate students and advanced undergraduates with Computational or Life Science backgrounds. Prior background in Biology is not required.

CSCI 2890. Comprehensive Examination Preparation .

For graduate students who have met the tuition requirement and are paying the registration fee to continue active enrollment while preparing for a preliminary examination.

CSCI 2950C. Topics in Computational Biology .

This course will investigate active and emerging research areas in computational biology. Topics include cancer genomics; genome rearrangements and assembly; and protein and regulatory interaction networks. The course will be a mixture of lectures and student presentations of recent conference and journal papers.

CSCI 2950D. Sensor Data Management .

Sensor networks combine sensing, computing, actuation, and communication in a single infrastructure that allows us to observe and respond to phenomena in the physical and cyber world. The sensors range from tiny "smart dusts" to dime-sized RFID tags and large-scale weather sensors. This course will cover the state-of-the art in designing and building sensor networks, focusing on issues that revolve around data and resource management. No prerequisites.

CSCI 2950E. Stochastic Optimization .

This advanced graduate course/seminar will focus on optimization under uncertainty, or optimization problems where some of the constrains include random (stochastic) components. Most practical optimization problems are stochastic (subject to future market conditions, weather, faults, etc.), and there has been substantial research (both theoretical and experimental) in efficient solution for such problems. We'll read and discuss some of the recent works in this area.

CSCI 2950F. Implementing Web-Based Software Systems .

CSCI 2950G. Large-Scale Networked Systems .

Explores widely-distributed systems that take advantage of resources throughtout the Internet. The systems leverage their large size and geographic diversity to provide bandwidth scalability, rapid responses, fault-tolerance, high-availability and diverse data collection. Topics include overlay networks, peer-to-peer systems, content distribution networks, distributed file systems and wide-scale measurement systems.

CSCI 2950H. Advanced Cryptography .

CSCI 2950I. Computational Models of the Neocortex .

This course addresses the problem of modeling the perceptual neocortex using probabilistic graphical models, including Bayesian and Markov networks, and extensions to model time and change such as hidden Markov models and dynamic Bayesian networks. The emphasis is on problems of learning, inference, and attention. Sources include the literature in computational and cognitive neuroscience, machine learning, and other fields that bear on how biological and engineered systems make sense of the world. Prerequisites: basic probability theory, algorithms and statistics.

CSCI 2950J. Cognition, Human-Computer Interaction and Visual Analysis .

In this graduate seminar we will learn about models of human cognition and perception, and explore potential implications of the models on how computers and humans can interact effectively when performing scientific analyses. Participants will be responsible for reading assigned materials, taking turns guiding discussions of the readings, and preparing a final paper and presentation. It is recommended that participants have some background in at least one of the areas of study.

CSCI 2950K. Special Topics in Computational Linguistics .

Every year will cover a different topic in computational linguistics, from a statistical point of view, including parsing, machine translation, conference, summarization, etc. Prerequisites: CSCI 1460 or permission of the instructor.

CSCI 2950M. Computer Science, Algorithms and Economics .

Course investigates the interplay of economic theory and computer science. It is suitable for advanced senior undergraduates and for graduate students. We will study topics such as: algorithms for selfish routing; competitive combinatorial auctions; Multicast cost sharing and cooperative games; graphical models for games; and related topics. This course will be organized around the presentation of recent research papers. Prerequisite: CSCI 1570 or equivalent.

CSCI 2950N. Special Topics in Autonomous Robotics .

No description available.

CSCI 2950O. Topics in Brain-Computer Interfaces .

Introduces the mathematical and computational foundations of brain-computer interfaces. Statistical learning, Bayesian inference, dimensionality reduction, information theory, and other topics are presented in the context of brain interfaces based on neural implants and EEG recordings. Prerequisites: Basic knowledge of probability, statistics and linear algebra (e.g., CSCI 1550 , APMA 1650 , APMA 1690 , or APMA 2640 ). Enrollment limited to 20 students.

CSCI 2950P. Special Topics in Machine Learning .

This seminar course explores current research topics in statistical machine learning. Focus varies by year, and may include Bayesian nonparametrics; models for spatial, temporal, or structured data; and variational or Monte Carlo approximations. Course meetings combine lectures with presentation and discussion of classical and contemporary research papers. Students will apply some this material to a project, ideally drawn from their own research interests.

CSCI 2950Q. Topics in Computer Vision .

This course will cover current topics in computer vision by focusing on a single real problem in computer vision. Recent courses have focused on forensic video analysis of an unsolved murder and three-dimensional object recognition for a mobile robot. Readings from the literature are integrated with group projects to solve problems beyond the state of the art. Strong mathematical skills (probability, linear algebra, calculus) and previous exposure to computer vision (e.g. CSCI 1430 ) are essential.

CSCI 2950R. Special Topics in Advanced Algorithms .

We will study an advanced topic in the design and analysis of algorithms. Prerequisite: CSCI 1570 or the equivalent.

CSCI 2950S. Advanced Practical Combinatorial Algorithms .

CSCI 2950T. Topics in Distributed Databases and Systems .

This course explores data and resource management issues that arise in the design, implementation, and deployment of distributed computing systems by covering the state of the art in research and industry. Typical topics include cloud computing and sensor networks. Strongly recommended: CSCI 0320 , CSCI 1270 , or CSCI 1951A .

CSCI 2950U. Special Topics on Networking and Distributed Systems .

Explores current research topics in networking, distributed and operating systems. Specific topics may include wireless and sensor networking, Internet-scale distributed systems, cloud computing, as well as the core problems, concepts, and techniques underlying these systems. The course has two components: reading and discussion of current and classical research papers, and a research project related to the topic but ideally drawn from students' own research interests. This is a graduate-level course, undergrads can join with the consent of the instructor.

CSCI 2950V. Topics in Applied Cryptography .

This course surveys recent developments in applied cryptography. Research in this field is motivated by privacy and security issues that arise in practice from areas like cloud computing, databases, surveillance and finance. Topics will vary each year. Pre Requisites: CSCI 1660 and CSCI 1510 recommended or instructor permission. This year's theme is cryptography for social good.

CSCI 2950W. Online Algorithms .

Decisions must often be made before the entire data is available. Online algorithms solve problems in which commitments must be made as the data is arriving. Choosing which items to evict from a cache before knowing future requests, which advertisers to consider for displaying ads alongside the result of a search, or which most representative data to store when computing statistics about a huge stream of information. We will discuss the worst-case model, which hinges against the worst possible future data, and some stochastic and game-theoretic models.

CSCI 2950X. Topics in Programming Languages and Systems .

Examines contemporary research topics in software construction from the perspectives of programming languages, software engineering and computer-aided verification. The primary goals are to understand which theory applies to which problems and to convert that theory into tools. Topics include security, modularity, and new paradigms in software composition. Prerequisite: CSCI 1730 or written permission of the instructor.

CSCI 2950Y. Theorem Proving .

This course explores computer-assisted theorem proving with the Coq Proof Assistant. The course will teach students to formally specify software and model mathematical theories. We will then study techniques for mechanically proving theorems about these Coq. Prerequisites: CSCI 1730 or CSCI 0170 and permission of the instructor.

CSCI 2950Z. Robot Learning and Autonomy .

This seminar course will cover current research topics related to perceiving and acting in the real world. These topics will be pursued through independent reading, class discussion, and project implementations. Papers covered will be drawn from robotics, computer vision, animation, machine learning, and neuroscience. Special emphasis will be given to developing autonomous control from human performance. No prerequisites.

CSCI 2951A. Robots for Education .

This seminar will explore the potential for robotics to engage future generations of scientists and engineers, with a particular focus on broadening participation in computing across society. Academic papers describing existing models, systems, courses, and evaluation for teaching robotics at undergraduate and secondary levels will be covered through students presentations. A group project will be conducted to find viable and accessible "off-the-shelf" technology solutions suited to teaching robotics without requiring a technical background. Instructor permission required.

CSCI 2951B. Data-Driven Vision and Graphics .

Investigates current research topics in image-based graphics and vision. We will examine data sources, features, and algorithms for understanding and manipulating visual data. We will pay special attention to methods that use crowd-sourcing or Internet-derived data. Vision topics such as scene understanding and object detection will be linked to graphics applications such as photo editing and image-based rendering. These topics will be pursued through independent reading, class discussion and presentations, and a semester long research project. Strong mathematical skills and previous imaging (vision or computational photography) courses are essential.

CSCI 2951C. Autonomous Agents and Computational Market Design .

An important area of research in artificial intelligence is how to effectively automate decision making in time-critical, information-rich environments. Electronic markets are a prime example of such environments. In this course students will create their own simulated electronic market as well as autonomous agents that trade in their market simulation. Application domains will include supply chain management, the Dutch flower auctions, and ad auctions, such as those run by Google and Facebook. Enrollment limited to 40 graduate students.

CSCI 2951E. Topics in Computer Systems Security .

This course explores advanced topics and highlights current research in computer security and privacy. Recent research papers will be presented and discussed. Also, projects will provide an opportunity for creative work. Class attendance is required and active participation in class discussions is essential. The course has two sections, each with a different focus and prerequisites. Section S01 (Networks, Software, and Systems) addresses computer security and privacy from the perspective of networks, software, and systems. Section S02 (Human Factors, Law, and Policy) addresses computer security and privacy from the perspective of law, policy, and human factors. Either section of the course can be used toward satisfying the capstone requirement for the ScB degree in Computer Science. Instructor permission is required to register.

CSCI 2951F. Learning and Sequential Decision Making .

The course explores automated decision making from a computer-science perspective. It examines efficient algorithms, where they exist, for single agent and multiagent planning as well as approaches to learning near-optimal decisions from experience. Topics will include Markov decision processes, stochastic and repeated games, partially observable Markov decision processes, and reinforcement learning. Of particular interest will be issues of generalization, exploration, and representation. Participants should have taken a graduate-level computer science course and should have some exposure to machine learning from a previous computer-science class or seminar; check with instructor if not sure. Recommended Prerequisites: CSCI 1950F or CSCI 1420

CSCI 2951I. Computer Vision for Graphics and Interaction .

Computer vision reconstructs real world information from image and video data; computer graphics synthesizes dynamic virtual worlds; interaction lets us explore these worlds; and machine learning allows us to map between domains across vision, graphics, and interaction. In visual computing, these fields converge to exploit both models of visual appearance and databases of examples to generate and interact with new images. This enables applications from the seemingly simple, like semantic photo editing, to the seemingly science fiction, like mixed reality. In this seminar, we will discover the state-of-the-art algorithmic contributions in computer vision which make this possible. Please join us!

CSCI 2951K. Topics in Collaborative Robotics .

Practical approaches to designing intelligent systems. Topics include search and optimization, uncertainty, learning, and decision making. Application areas include natural language processing, machine vision, machine learning, and robotics. Prerequisite: CSCI 1410 , 1420 , 1460 , 1480 , or 1950F; or instructor permission.

CSCI 2951M. Advanced Algorithms Seminar .

Students in this course will read, present, and discuss recent breakthrough papers on the topic of algorithms, and the related areas needed to analyze algorithms. This course is aimed at current and potential future graduate students who want to gain technical depth and perspective on the field of algorithms. Topics will roughly alternate by year, with even years emphasizing fundamental techniques, and odd years emphasizing applications such as machine learning. Suggested prerequisites: CSCI 1570 and mathematical maturity. Instructor permission required. Enrollment will be limited to 24 students, based on an application that will be described on the first day of class. Ideal students will have a mix of the following: 1) motivation to learn how to read papers, 2) technical skills and background, 3) willingness to participate and contribute to discussions.

CSCI 2951N. Advanced Algorithms in Computational Biology .

This is a full-lecture, graduate course on algorithms and biomedical applications. The Foundations lectures are an introduction to the biological and medical genomics application areas. Each Algorithm section is devoted to an algorithmic method presented in rigorous depth, followed by an important open problem in the application area, together with the current most effective algorithmic solutions to the problem. Graduate students and advanced undergraduates in computational and mathematical sciences and engineering are welcome. Biological, life sciences and medical students and faculty are welcome as well and will be able to participate more in the applications areas.

CSCI 2951O. Foundations of Prescriptive Analytics .

We are undoubtedly in the middle of an Analytics Revolution that enabled turning huge amounts data into insights, and insights into predictions about the future. At its final frontiers, Prescriptive Analytics is aimed at identifying the best possible action to take given the constraints and the objective. To that end, this course provides students with a comprehensive overview of the theory and practice of how to apply Prescriptive Analytics through optimization technology. A wide variety of state-of-the-art techniques are studied including: Boolean Satisfiability, Constraint Programming, Linear Programming, Integer Programming, Local Search Meta-Heuristics, and Large-Scale Optimization. Pre Requisites: One of CSCI 0300 , 0320 , CSCI 0330 , CSCI 1310 , OR CSCI 1330 and recommended: one of CSCI 0530 , CSCI 1570 , MATH 0520 or MATH 0540 .

CSCI 2951S. Distributed Computing through Combinatorial Topology .

Although computer science itself is based on discrete mathematics, combinatorial topology and its applications may still be unfamiliar to many computer scientists. For this reason, this course provides a self-contained, elementary introduction to the concepts from combinatorial topology needed to analyze distributed computing. Conversely, while the systems and models used here are standard in computer science, they may be unfamiliar to students with a background in pure or applied mathematics. For this reason, this course also provides a self-contained, elementary description of standard notions of distributed computing. CSCI 0220 required, CSCI 1760 recommended

CSCI 2951T. Data-Driven Computer Vision .

Investigates current research topics in data-driven object detection, scene recognition, and image-based graphics. We will examine data sources, features, and algorithms useful for understanding and manipulating visual data. We will pay special attention to methods that harness large-scale or Internet-derived data. There will be an overview of the current crowdsourcing techniques used to acquire massive image datasets. Vision topics such as scene understanding and object detection will be linked to graphics applications such as photo editing. These topics will be pursued through independent reading, class discussion and presentations, and projects involving current research problems in Computer Vision.

CSCI 2951U. Topics in Software Security .

This course investigates the state-of-the-art in software exploitation and defense. Specifically, the course is structured as a seminar where students present research papers to their peers. We will begin with a summary of prevalent software defects, typically found in applications written in memory unsafe languages, and proceed to surveying what we are up against: traditional and modern exploitation techniques, ranging from classical code injection and code reuse up to the newest goodies (JIT-ROP, Blind ROP). For the bulk part, we will focus on the latest advances in protection mechanisms, mitigation techniques, and tools against modern vulnerability classes and exploitation methods.

CSCI 2951X. Reintegrating AI .

The goal of AI has been to build complete intelligent agents, yet the field has been fragmented into a collection of problem-specific areas of study. We will first spend a few weeks in lecture covering a new approach to integrating existing AI subfields into a single agent architecture, and remainder of the semester on self-directed, semester-long research projects. Grading based on a mid-semester project proposal, and a substantial open-ended final project. The projects will be multi-disciplinary in nature but students will have the opportunity to work in small groups, so they need not necessarily have expertise in the relevant areas. Graduate students welcome; undergraduates need instructor permission to enroll.

CSCI 2951Z. Advanced Algorithmic Game Theory .

This course examines topics in game theory from a computer scientist's perspective. Through the lens of computation, it will focus on the design and analysis of systems involving self-interested agents, investigating how strategic behavior should influence algorithm design, which game-theoretic solution concepts are practical to implement, and the ramifications of conflicts of interest between system designers and participating agents. Students will create their own automated trading agents for various simulated market games. Topics include: auctions and mechanism design, equilibria, and learning. For graduate credit, students will complete additional homework exercises, and a significant programming project.

CSCI 2952B. Topics in Computer Science Education Research .

How do people learn computing, and what can we do to teach them better? Answering these questions requires applying techniques from a variety of disciplines: computer science, naturally, but also cognitive science, psychology, linguistics, sociology, and more—even fields like economics can be relevant. This course studies different focused topics in computing education research (CER), drawing on these other disciplines as needed.

CSCI 2952C. Learning with Limited Labeled Data .

As machine learning is deployed more widely, researchers and practitioners keep running into a fundamental problem: how do we get enough labeled data? This seminar course will survey research on learning when only limited labeled data is available. Topics covered include weak supervision, semi-supervised learning, active learning, transfer learning, and few-shot learning. Students will lead discussions on classic and recent research papers, and work in teams on final research projects. Previous experience in machine learning is required through CSCI 1420 or equivalent research experience.

CSCI 2952F. Distributed Systems at Scale: Microservices Management .

This seminar investigates and explores cutting edge challenges and issues in the emerging Microservices paradigm. Microservices are a specific cloud paradigm for enabling distributed systems and applications at scale. In particular, this course builds on the foundations provided by the initial distributed systems, networking and operating systems offering (i.e., CSCI 1380 , CSCI 1680 , CSCI 1670 ) and explores how these concepts are used to realize, manage, and orchestrate microservices. The course is driven by materials from academic conferences and industrial blogs. The industrial blogs will provide context and motivation for different problems. The academic reasons will provide a deep divide into the technical details: we will focus on reading, analyzing, critiquing and brainstorming academic papers. Students taking this class should be familiar with reading academic literature, performing critical analysis, and working on open ended problems with undefined solutions. More information: http://cs.brown.edu/courses/info/csci2952-f/

CSCI 2952G. Deep Learning in Genomics .

Deep learning models have achieved impressive performance in fields like computer vision and NLP. Given an adequate amount of data, these models can extract meaningful representations to perform accurate predictions. The collection of vast quantities of biological data naturally leads to the question -- can deep learning help us understand genomics? In this seminar-style class, we will cover the recent research literature trying to answer this question. We will learn how state-of-the-art models like CNNs, RNNs, GCNs, GANs, etc. have been applied to solve significant problems in genomics and what unique challenges are presented by the data in this field.

CSCI 2952H. Recent Progress in Reinforcement Learning .

Reinforcement learning is a framework for studying machines that interact with a sequential environment to achieve a goal. In the past decade, the RL framework has gained a lot of attention owing to its intriguing success in solving problems in complicated domains such as games, robotics, and dialog systems. We observe continual growth in the number of RL papers published in major machine-learning conferences. This growth calls for a careful investigation of the recent progress in the field. By reading selections of the current RL literature, this graduate-level course examines some of the latest theoretical and empirical progress in the field.

CSCI 2952I. Language Processing in Humans and Machines .

Understanding language requires transforming sequences of sounds into words, combining words into meaningful thoughts, and incorporating thoughts into an ongoing discourse. Psychologists and linguists have been trying to reverse-engineer how humans do this so easily, at the speed of conversation. In parallel, computer scientists have been trying to engineer machines to solve the same problems, leading to products like Siri and Alexa. This class will explore how these two kinds of research can help each other, bringing recent insights from machine learning into the study of human language processing, and insights from human processing into the architectures of machine language systems. For CS students: Machine Learning, Deep Learning, Computational Linguistics (or comparable experience). For CLPS students: At least one of CLPS 0200 , 0300, 0800 , or 1800

CSCI 2952K. Topics in 3D Computer Vision and Deep Learning .

We live in a world that spans 3 dimensions. Cameras and sensors image the 3D world by projecting to a 2D plane. How can we recover the 3D world back from these images? What techniques can we use to process 3D data? In this course we will study computer vision and machine learning techniques to recover 3D information of the world from images, and process and understand 3D data. We will learn about classical computer vision techniques but focus on cutting-edge deep learning methods. The techniques we will study are widely used, for instance, in self-driving cars and smartphone AR face filter apps.

CSCI 2952N. Advanced Topics in Deep Learning .

Prepares graduate students with the knowledge they need to apply Deep Learning techniques for their own research. There has been tremendous success in developing unified neural architectures that achieve state-of-the-art performance on language understanding (GPT-3), visual perception (ViT), and even protein structure prediction (AlphaFold). We plan to understand how they work, and how the success of such unified models can give rise to further developments on self-supervised learning, a technique that trains machine learning models without requiring labeled data; and multimodal learning, a technique that utilizes multiple input sources, such as vision, audio, and text. We will study recent attempts to interpret these models, thus revealing potential risks on model bias. Paper reading, student presentations, and invited guest lectures. Students required to work on a final project that explores a novel direction along the line of the papers we cover.

CSCI 2952O. A Practical Introduction to Advanced 3D Robot Perception .

This course is aimed at preparing graduate students and senior undergrads to do advanced work at the intersection of two important and popular fields: computer vision and robotics. The course will focus on the latest advances through lectures, readings, and discussion groups. The lectures and readings will be designed to represent a mix of classical techniques as well as the most recent advances in the two fields. The unique highlight of this course is the inclusion of a practical component: students will implement a project that combines computer vision and robotics by using cameras and a real robot arm. Students will form teams for this project and have exclusive access to a camera and a small robot arm both of which can be interfaced with the students' laptops. Pre-reqs: One of CSCI 1430 , CSCI 1470 , CSCI 1951R , CSCI 1230 .

CSCI 2952P. Coordinated Mobile Robotics (ENGN 2912U) .

Interested students must register for ENGN 2912U .

CSCI 2952Q. Robust Algorithms for Machine Learning .

As machine learning systems start to make more important decisions in our society, we need learning algorithms that are reliable and robust. In this course, we will (1) cover basic tools in linear algebra, matrix calculus, and statistics that are useful in theoretical machine learning, (2) explore different adversarial models and examine whether existing algorithms are robust in these models, and (3) design and analyze provably robust algorithms for fundamental tasks in machine learning. In particular, we will focus on the research areas of high-dimensional robust statistics, non-convex optimization, learning with strategic agents, and spectral graph theory. This is a research-oriented course where students are asked to read and present sophisticated papers in top machine learning conferences. Knowledge of basic linear algebra, algorithms, data structures, probability and statistics is essential. Prior experience with machine learning is required.

CSCI 2952S. Topics in Cyber and Digital Policy .

This online asynchronous course explores advanced topics in cybersecurity and digital policy, including privacy and civil liberties. Research papers and/or projects will provide an opportunity for creative work. Topics may include public policy and the international aspects of cybersecurity, legislation and legal requirements concerning digital policy issues, the history and background of privacy and civil liberties in information and information systems, cyber conflict, and related subjects. There is no set class time. Students and the instructor will coordinate work online and over Zoom through periodic meetings. CSCI 1800 , 1860 , 1805 , 1870 or equivalent background is a prerequisite. Instructor permission required.

CSCI 2955. The Design and Analysis of Trading Agents .

The Dutch Flower Auctions (DFA) clear over 100,000 auctions per day, each lasting on average between 3 and 5 seconds! This semester, we'll study the mechanism through which the DFA distribute 2/3 of the world's flowers, focusing on both the sellers' and buyers' decision-making processes. More generally, we'll research ways to automate and optimize decision-making in time-critical, information-rich environments, like the DFA. Undergraduate students require instructor permission, and should have already completed CSCI 0190 , or CSCI 0150 and CSCI 0160 , or CSCI 0170 and CSCI 0180 .

CSCI 2956A. Design of Agents for Bidding in Sponsored Search Autions .

This course investigates the new field of sponsored search auctions. Although students will be exposed to the field from the point of view of both the search engine and the advertiser, the course's focus is on advertiser's bidding algorithms. The students will implement novel bidding agents, and the course will culminate in a competition among the students' agents. Undergraduate students who obtained permission from the instructor or completed CSCI 0910, or CSCI 0150 and CSCI 0160 , or CSCI 0170 and CSCI 0180 can register for the course. CSCI 1410 is a co-requisite.

CSCI 2956R. Multiplicative-Weights/Packing-Covering Method for Approximating Linear and Semidefinite Programs .

We will study the method called, variously, multiplicative weights and packing-covering. We will in particular investigate the use of this method for finding approximately optimal solutions to linear programs and semidefinite programs. Prerequisite: A graduate-level course on algorithms. Enrollment limited to 10. Instructor permission required.

CSCI 2980. Reading and Research .

Section numbers vary by instructor. Please check Banner for the correct section number and CRN to use when registering for this course.

CSCI 2990. Thesis Preparation .

For graduate students who have met the residency requirement and are continuing research on a full time basis.

CSCI 2999A. Cybersecurity Management Within Business, Government, and Non-Profit Organizations .

This class will put you in the shoes of decision-makers working in business, government, and non-profit sectors so that you can gain experience grappling with real-world problems through thought exercises and thoughtful reflection. I anticipate that the course will feature guest speakers that will help you learn unique insights. This is a fully online course designed to help you develop your understanding of cybersecurity management challenges and opportunities that exist within public, non-profit, and private sectors. Content is designed to strengthen your skills as both a cybersecurity practitioner and leader. By examining real events that organizations have faced, you will practice and refine your ability to evaluate the challenges that arise for organizational leaders. Undergraduate and graduate students prepared for a variety of post-graduation professional roles. Cybersecurity students will similarly gain practical skills that will assist them in cybersecurity leadership roles. Pre Requisites: CSCI 1360 or enrollment in one of the specified concentrations

CSCI XLIST. Courses of Interest to Concentrators in Computer Science .

Computer Science-Economics

Applied mathematics-computer science, mathematics-computer science.

Computer science is now a critical tool for pursuing an ever-broadening range of topics, from outer space to the workings of the human mind. In most areas of science and in many liberal arts fields, cutting-edge work depends increasingly on computational approaches. The undergraduate program at Brown is designed to combine breadth in practical and theoretical computer science with depth in specialized areas. These areas range from traditional topics, such as analysis of algorithms, artificial intelligence, databases, distributed systems, graphics, mobile computing, networks, operating systems, programming languages, robotics and security, to novel areas including games and scientific visualization.

Our requirements are built on a core set of foundations ,  each course representing an essential area within computer science.  Concentrators choose the upper-level courses that align with their interest. Students may not use more than two CSCI 1970 courses to complete the requirements for the Sc.B. and one CSCI 1970 course for the A.B. requirements.

For up-to-date information on our concentration requirements please see https://cs.brown.edu/degrees/undergrad/concentrating-in-cs/concentration-requirements-2024. Please see https://cs.brown.edu/degrees/undergrad/concentrating-in-cs/concentration-handbook/ for additional information regarding our concentration requirements (including allowed substitutions and policies). 

Requirements for the Standard Track of the Sc.B. degree

Requirements for the standard track of the a.b. degree, requirements for the professional track of the both the sc. b. and a.b. degrees..

The requirements for the professional track include all those of the standard track, as well as the following:

Students must complete full-time professional experiences doing work that is related to their concentration programs, totaling 2-6 months, whereby each internship must be at least one month in duration in cases where students choose to do more than one internship experience. Such work is normally done at a company, but may also be at a university under the supervision of a faculty member. Internships that take place between the end of the fall and the start of the spring semesters cannot be used to fulfill this requirement.

On completion of each professional experience, the student must write and upload to ASK a reflective essay about the experience addressing the following prompts, to be approved by the student's concentration advisor:

  • Which courses were put to use in your summer's work? Which topics, in particular, were important?
  • In retrospect, which courses should you have taken before embarking on your summer experience? What are the topics from these courses that would have helped you over the summer if you had been more familiar with them?
  • Are there topics you should have been familiar with in preparation for your summer experience, but are not taught at Brown? What are these topics?
  • What did you learn from the experience that probably could not have been picked up from course work?
  • Is the sort of work you did over the summer something you would like to continue doing once you graduate? Explain.
  • Would you recommend your summer experience to other Brown students? Explain.

Honors candidates must have earned A's or S-with-distinction in 2/3 (rounding up) of the courses used towards the concentration, excluding introductory-sequence courses (CS courses numbered 0200 or below) and the calculus prerequisite.

The joint Computer Science-Economics concentration exposes students to the theoretical and practical connections between computer science and economics. It prepares students for professional careers that incorporate aspects of economics and computer technology and for academic careers conducting research in areas that emphasize the overlap between the two fields. Concentrators may choose to pursue either the A.B. or the Sc.B. degree. While the A.B. degree allows students to explore the two disciplines by taking advanced courses in both departments, its smaller number of required courses is compatible with a liberal education. The Sc.B. degree achieves greater depth in both computer science and economics by requiring more courses, and it offers students the opportunity to creatively integrate both disciplines through a design requirement. If you are interested in declaring a concentration in Computer Science-Economics, please refer to this page for more information regarding the process. For more information about the CS Pathways, see this  page.

Standard Program for the Sc.B. degree.

Standard program for the a.b. degree:.

Students who meet stated requirements are eligible to write an honors thesis in their senior year.  Students should consult the listed honors requirements of whichever of the two departments their primary thesis advisor belongs to, at the respective departments' websites. If the primary thesis advisor belongs to Economics (Computer Science), then students must have a reader in the Computer Science (respectively, Economics) department.

Professional Track

The Sc.B. concentration in Applied Math-Computer Science provides a foundation of basic concepts and methodology of mathematical analysis and computation and prepares students for advanced work in applied mathematics, computer science, and data science. Concentrators must complete courses in mathematics, applied math, computer science, and an approved English writing course. While the concentration in Applied Math-Computer Science allows students to develop the use of quantitative methods in thinking about and solving problems, knowledge that is valuable in all walks of life, students who have completed the concentration have pursued graduate study, computer consulting and information industries, and scientific and statistical analysis careers in industry or government. This degree offers a standard track and a professional track.

Professional Tracks

The requirements for the professional tracks include all those of each of the standard tracks, as well as the following:

Students must complete full-time professional experiences doing work that is related to their concentration programs, totaling 2-6 months, whereby each internship must be at least one month in duration in cases where students choose to do more than one internship experience. Such work is normally done at a company, but may also be at a university under the supervision of a faculty member. Internships that take place between the end of the fall and the start of the spring semesters cannot be used to fulfill this requirement.

On completion of each professional experience, the student must write and upload to ASK a reflective essay about the experience, to be approved by the student's concentration advisor,  addressing these questions:

  • Which courses were put to use in your summer's work?  Which topics, in particular, were important?
  • In retrospect, which courses should you have taken before embarking on your summer experience?  What are the topics from these courses that would have helped you over the summer if you had been more familiar with them?
  • Are there topics you should have been familiar with in preparation for your summer experience, but are not taught at Brown?  What are these topics?

Concentrators that demonstrate excellence in grades and in undergraduate research can be awarded departmental honors. Honors students with primary advisors in Applied Math should follow the guidelines, requirements, and deadlines for honors as described in the bulletin for Applied Math concentrators and as published on the APMA departmental website . Honors students with primary advisors in Computer Science should follow the guidelines, requirements, and deadlines for honors as described in the  bulletin for Computer Science concentrators and as published on the  CS departmental website . Students wishing to do honors research with a non-APMA or CS advisor should contact the Directors of Undergraduate Studies in APMA and CS to discuss options.

Students may opt to pursue an interdisciplinary Bachelor of Science degree in Math-Computer Science, a concentration administered cooperatively between the mathematics and computer science departments. Course requirements include math- and systems-oriented computer science courses, as well as computational courses in applied math. Students must identify a series of electives that cohere around a common theme. As with other concentrations offered by the Computer Science department, students have the option to pursue the professional track of the ScB program in Mathematics-Computer Science.

Requirements for the Standard Track of the Sc.B. degree.

Requirements for the Professional Track of the Sc.B. degree.

Cybersecurity

The department of Computer Science offers two graduate degrees in computer science. The Master of Science (Sc.M.) degree for those who wish to improve their professional competence in computer science or to prepare for further graduate study, and the Doctor of Philosophy (Ph.D) degree.

For more information on admission, please visit the following website:

http://www.brown.edu/academics/gradschool/programs/computer-science

Ph.D. Requirements

Requirements for the Ph.D. program can be found at  https://cs.brown.edu/degrees/doctoral/reqs/reqs_phd.2015.pdf

Requirements for the Masters Degree

The requirements consist of a basic component and an advanced component. All courses must be at the 1000 level or higher. All courses must be completed with a grade of B or better.

The courses in student's program must be approved by the director of the Master's program (as well as by the student's advisor).

Basic Component

The basic component consists of six courses. None of these courses may be reading and research courses such as  CSCI 2980 .

The six courses are chosen as follows:

  • Two must be CS courses that form a pathway (see the explanation of pathways at https://cs.brown.edu/degrees/undergrad/concentrating-in-cs/concentration-requirements-2020/pathways-for-undergraduate-and-masters-students/
  • One must be a CS course in an area that’s not listed in the chosen pathway (it must not be a core course, must not be a grad course, and must not be a related course of the pathway).
  • The three additional courses must be in CS or related and must be approved by your advisor or the director of graduate studies (master’s). Getting this approval will require you to show that the courses are relevant to your CS interests. In general, the more non-CS courses you wish to take, the stronger your justification must be. 

Advanced Component

The advanced component requires the student to complete one of the following six options. Reading and research courses (such as CSCI 2980 ) may be used as part of options 1, 2, 3, and 4, but not as part of options 5 and 6. An “advanced course,” as used below, is either a 2000-level CS courses or a 1000-level CS courses that includes a Master's supplement. Master's supplement are nominally half-credit courses, but students may do the work of these courses without officially registering for them. Examples of such supplements are CSCI 1234 (supplementing CSCI 1230 ), CSCI 1690 (supplementing CSCI 1670 ), and CSCI 1729 (supplementing CSCI 1730 ).

“Internships”, as used below, must be approved by the student's advisor and are paid work in the area of the student's master's studies. They may be full, or part time. A full-time internship must last at least two months but no more than four months. A part-time internship must last at least four months but no more than six months. Normally the internship will be performed between the student's second and third semesters in the program.

The six options are:

  • Complete a thesis supervised by her or his advisor and approved by a committee consisting of the advisor and at least one other faculty member.
  • Complete a thesis supervised by her or his advisor and approved by a committee consisting of the advisor and at least one other faculty member, and complete an internship.
  • Complete a project supervised and approved by her or his advisor.
  • Complete a project supervised and approved by her or his advisor, and complete an internship.
  • Complete two advanced courses.
  • Complete two advanced courses and complete an internship.

Students entering the Master's program typically have one of two goals: they intend to pursue research in Computer Science and are preparing themselves to enter Ph.D. programs, or they intend to become professional computer scientists and pursue careers in industry. In both cases, students should take collections of courses that not only give them strength in particular areas of Computer Science, but also include complementary areas that familiarize them with other ways of thinking about the field. For example, a student whose interests are in the practical aspects of designing computer systems should certainly take courses in this area, but should also be exposed to the mindset of theoretical computer science. In a rapidly changing discipline, there is much cross-fertilization among areas and students should have some experience in doing advanced work in areas not directly related to their own.

A student whose goal is a research career should become involved as quickly as possible with a research group as part of their Master's studies, and demonstrate and learn about research by participating in it. The resulting thesis or project report will serve to establish her or his suitability for entering a Ph.D. program.

A student whose goal is to be a professional computer scientist should have some professional experience as part of her or his preparation. A certain amount of coursework is required before a student can qualify for a pedagogically useful internship. Students with limited experience in Computer Science should take a few advanced Computer Science courses before embarking on an internship. Other students, particularly those whose undergraduate degrees were at Brown, will have had internship experiences while undergraduates. Internships provide insights for subsequent courses and project work at Brown. Students without such experiences are at a disadvantage with respect to their peers. Thus we strongly encourage students who have not had such experience to choose of of options 2, 4, and 6, for which internships are required.

Note that these internships are not courses and the work is not evaluated as it would be for a course. Students' advisors will assist them in choosing and obtaining internships, but it is up to students themselves to ensure that they get as much benefit as possible from their experiences. They must be able to take advantage of these experiences while completing their Master's projects – we expect as high-quality work from them as we do from students who entered the program with prior internship experiences.

A Master's degree normally requires three to four semesters of full-time study, depending upon one's preparation. 

Concurrent ScB (NUS) and ScM in Computational Biology (Brown University)

The School of Computing at National University of Singapore and The Department of Computer Science at Brown have established a concurrent Bachelor’s and Master’s degree program in Computational Biology. After having first completed four years of under- graduate study at National University of Singapore (NUS), qualified students will attend Brown University to complete their fifth and final year of study in computational biology. After the successful completion of requirements set forth by both universities, the students will simultaneously earn both their Sc.B. and Sc.M. degrees. The Sc.B will be awarded by the National University of Singapore, while the Sc.M. is awarded by Brown University.

Requirements for the Master of Science in Cybersecurity

The Master of Science in Cybersecurity is designed to be completed in 4 semesters. It takes 8 courses to complete the program and students can take up to 3 courses per semester, but the Department strongly recommends taking no more than 2 courses per semester--especially during one's first term at Brown. We do not currently offer summer term courses in the program, so courses are completed during fall and spring. Course availability varies and there is no guarantee that students will be able to take every course they are interested in.

Each Cybersecurity student must have their course choices approved by the Director of Graduate Studies for their Track and by the student's advisor. Cybersecurity students must only register for courses at the 1000-level or higher only. Additionally, all courses must be completed with a grade of B or better. Furthermore, students must complete at least two 2000-level courses.

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Graduate Programs

Biostatistics.

The doctoral program in Biostatistics provides the training necessary to carry out independent research in theory, methodology and the application of statistics to important problems in biomedical research, including research biology, public health and clinical medicine.

The Ph.D. program is administered by an active, expanding and highly interdisciplinary faculty in the Department of Biostatistics. Major areas of research activity include Bayesian inference, analysis of biomarkers and diagnostic tests, causal inference and missing data, time series and functional data analysis, modeling of social networks, bioinformatics, longitudinal data, and multilevel modeling. Faculty collaborate actively with investigators in the areas of cancer prevention and screening, behavioral sciences, HIV/AIDS, health care policy, genetic epidemiology, neuroscience, and genomics.

Additional Resources

All PhD graduate students are provided with a new laptop computer and office space.  Students also have access to the computing infrastructure at the Center for Statistical Sciences, a high-end, continuously updated computing environment featuring both Unix and PC/MAC networks, with access to all major software for data analysis and numerical computing. CSS also maintains a considerable collection of statistics texts and journals in the Walter Freiberger Biostatistics Library.

Application Information

MCAT or LSAT tests cannot be substituted for the GRE. Applicants to the Ph.D. program should have taken courses in calculus (three semesters), and advanced undergraduate courses in linear algebra and probability. Experience with numerical computing is also recommended. Applications from students in applied fields such as biology, biochemistry, economics, and computer science are strongly encouraged, with the understanding that necessary mathematical coursework may have to be completed before or soon after enrollment in the program.

Applicants to this School of Public Health program should apply through  SOPHAS , a centralized application service for accredited schools and programs in public health. Brown University School of Public Health GRE reporting code: 7765.

Application Requirements

Gre subject:.

Not required

GRE General:

Official transcripts:, letters of recommendations:.

(3) Required

Personal Statement:

Additional materials:.

Application Fee

Additional Requirements:

International applicants.

  • Language Proficiency (TOEFL or IELTS if applicable)
  • Transcript Evaluation (if applicable)

Dates/Deadlines

Application deadline, completion requirements.

For all Ph.D. students, 24 credits are required of students matriculating in the program without a master's degree; 16 are required beyond the master's. For those with a related master's degree, up to eight units can be transferred. Both written and oral exams, plus a dissertation comprising an original contribution to the field, also are required. Students are expected to participate in academic activities such as the Statistics Seminar and faculty–organized working groups.

Alumni Careers

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Contact and Location

Department of biostatistics, mailing address.

  • Program Faculty
  • Program Handbook
  • Graduate School Handbook
  • Public Health Career Outcomes

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Online Master of Science in Biostatistics: Health Data Science Concentration

Leverage Data to Impact Science

Request Program Info

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2024-2025 Tuition rate can be found here . Tuition rates are subject to change each academic year.

Improve outcomes through data and analysis

Gain difference-making knowledge and skills by earning your Master of Science in Biostatistics 100% online from Brown University. Guided by expert Brown faculty and informed by the most up-to-date methods and data-driven technologies, you’ll develop expert-level proficiency in statistical methods, big health data and machine learning —all to develop impactful contributions across interdisciplinary fields in human health and life sciences.

In as little as 20 months, you can graduate ready to further your career, having increased your capacity to:

  • Draw statistical inferences from biomedical and health-related data at the individual and population levels.
  • Learn advanced statistical methodologies to conduct comprehensive data analysis.
  • Use statistical software for data management, implementation of statistical analysis and presentation of results.
  • Communicate comprehensive and novel statistical analysis of health data.

Important Dates and Deadlines

Spring 2025.

  • Application Opens: March 4, 2024
  • Early Action Deadline: July 15, 2024
  • Priority Deadline: September 15, 2024
  • Final Deadline: October 15, 2024
  • Semester Starts: January 22, 2025
  • Application Opens: October 16, 2024
  • Early Action Deadline: March 15, 2025
  • Priority Deadline: May 1, 2025
  • Final Deadline: June 1, 2025
  • Semester Starts: September 3, 2025

Spring 2025 Application Now Open! Final Deadline: October 15

Apply Today

Take your place at the forefront of Health Data Science

phd in computer science at brown university

Built for busy adults

At Brown, we’ve designed our 100% online Biostatistics master’s program to broaden the reach to students looking to advance their careers.

This program is especially ideal for:

  • Professionals with 2+ years of work experience seeking a more influential career path.
  • Individuals pursuing careers as professional biostatisticians or data analysts across interdisciplinary fields in human health and life science.
  • Full-time professionals seeking a flexible master’s degree in biostatistics and health data science.
  • Data-minded individuals who seek expertise in statistical methods, big health data, machine learning and more.
  • International students who are unable to relocate to the United States, who desire a prestigious Ivy League graduate education.

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Lead in a rapidly growing field

Careers related to health data science projected to be among the fastest-growing occupations in the United States over the next decade— about 10 times higher than the national average , per the Bureau of Labor Statistics. Thanks to Brown’s stellar reputation and rigorous curriculum, you can position yourself to take a leading role and make an impact in this increasingly important career field.

Rigorous, career-forward courses

To complete Brown’s 100% online Biostats program, you must complete 9 courses for a total of 36 credit hours, including a solutions-oriented capstone experience built on a real-world healthcare challenge.

Online Biostatistics Master’s Curriculum: Health Data Science Concentration

  • BHDS 2000: Probability and Statistical Inference
  • BHDS 2010: Statistical Programming for Health Data Science
  • BHDS 2110: Methods I: Linear and Generalized Linear Models
  • BHDS 2020: Design of Observational and Experimental Studies
  • BHDS 2120: Methods II: Extensions to Regression
  • BHDS 2030: Causal Inference
  • BHDS 2130: Methods III: Statistical Machine Learning
  • BHDS 2040: Advanced Topics in Health Data Science
  • BHDS 2050: Problem-driven Biostatistics and Capstone Project

Convenient curriculum delivery

As you progress through Brown’s 100% online Biostats program, you’ll complete your coursework asynchronously — learning on your own schedule — while also enjoying opportunities to connect with classmates and instructors when needed.

Watch and interact with faculty-created interactive multimedia, faculty-recorded lectures and demonstrations, expert/guest lecture videos and health data science cases.

Weekly online synchronous class sessions, though optional, help you connect with your instructors and classmates to get answers to your questions and enrich your point of view by understanding their varied perspectives.

Get More Course Info

World-class learning, accessible anywhere

Direct access to internationally renowned faculty.

Brown is home to some of the world’s foremost experts in clinical science and public health. As an online Biostatistics student, these experts serve as your instructors — helping you develop knowledge and skills through personal interaction and a rigorous curriculum.

Real data, real application, real solutions

Our online master’s in biostatistics program provides you with the same core biostatistical skills as our campus-based program. In addition, the online health data science concentration is designed to allow learners to develop highly sought-after foundational knowledge in health data science. Other benefits you can enjoy through learning at Brown include:

  • Tapping into the expertise of Brown faculty who actively address these issues in their research and careers.
  • Enhance your own point of view through the distinct perspectives and insights gained from your diverse classmates.
  • Strengthen your sense of community through regular collaboration — no matter where you’re physically located.

Learn More About Our Online Approach

Recognized for quality

The Brown University School of Public Health is accredited by the Council on Education in Public Health (CEPH). Like all CEPH-accredited programs, Brown’s 100% online Biostatistics master’s will teach and assess standard foundational competencies of public health, as well as health data science-specific competencies in analysis, leadership and communication.

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I've become a better problem solver, approaching complex projects more systematically than I used to. My critical thinking abilities have also definitely improved, complementing the many hard skills I’ve learned during classes.

- Hannah Eglinton, Brown Biostatistics ScM Student

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Vamshi Kiran Gogi at the Crater Lake in Oregon

Engineering doctoral student leads cutting-edge semiconductor work

Nsf and intel corp. funds work of uc electrical engineering student.

headshot of Lindsey Osterfeld

Vamshi Kiran Gogi always wanted to be an engineer. During the first semester of his master's program at the University of Cincinnati, he developed a passion for semiconductor research, leading him to transition into a doctoral program. 

Throughout his years as a Bearcat, Gogi has served as the president of the Electrical Engineering and Computer Science Graduate Student Association, trained students in cleanroom processes, acted as a graduate assistant in the Office of College Computing and more.

Gogi was named Graduate Student Engineer of the Month by UC's College of Engineering and Applied Science. 

Why did you choose UC?

Vamshi Kiran Gogi is researching solutions for the next generation of computing components.

As someone with a bachelor's degree in electrical and electronics engineering, I have always been fascinated by the field of semiconductors. The wide range of applications in electronics, communication systems, power electronics, the automotive industry, medical devices and consumer appliances has always intrigued me. Whether it's delving into materials or exploring devices within this field, my goal has been to deepen my understanding of these crucial electronic components, often regarded as "the brain of modern electronics." 

Among the offers I received as an applicant back in 2016, there were many facets of the University of Cincinnati that I found appealing. Particularly the fact that it is a tier 1 research institution, has strong academic programs, the diverse architecture of the campus and the well-established co-op program . I came to UC for the Master of Engineering (MEng) program during which I acquired a taste for research and transitioned to a Master of Science (MS) program. Having gained knowledge of electronic materials through my master's thesis work, I wanted to work on the applications of these materials. I decided to pursue a PhD for the opportunity to work on devices. The transition to a research-focused track was very smooth because of UC's cutting-edge research across different fields and state-of-the-art facilities. UC's affordability — especially the graduate incentive awards — tied with Cincinnati's affordable living costs made UC an easy choice for my studies. 

Why did you choose your field of study?

After getting my undergraduate degree in India, my journey at UC started in the fall of 2016 as a master's of engineering student studying advanced materials, devices and microsystems. In this program I was introduced to the multi-faceted nature of the field of semiconductors. Having been involved in several multidisciplinary projects, I started developing an appetite for research and transitioned to a master of science program under the advisement of Dr. Punit Boolchand . 

During this period, I acquired a thorough and deep understanding of semiconductor physics as well as the knowledge of multiple material characterization techniques. After learning about electronic materials, I aspired to delve into the practical applications of them. This research focus facilitated my transition into a more specialized PhD program in electrical engineering under the guidance of Dr. Rashmi Jha . I have been actively engaged in cutting-edge research within the field of logic and memory devices, contributing to the advancement of knowledge and the resolution of significant challenges. 

Briefly describe your research work. What problems do you hope to solve?

I am currently involved in researching solutions for the next generation of computing components. The focus is on enabling intelligent storage and efficient implementation of artificial intelligence and machine learning through in-memory computing. This work encompasses conducting a comprehensive literature review, gaining insights into existing work in the field, developing novel material deposition techniques, integrating them into novel device architectures through nano/microfabrication, conducting electrical and physical testing, and employing modeling techniques. This intricate research holds the potential to propel semiconductor and microelectronics technology to the next level. My research efforts are partially funded by Intel Corp.'s  CAFÉ program and the National Science Foundation.

At UC, Vamshi Kiran Gogi trains students in clean room processes, among other involvements. Photo/Corrie Mayer/CEAS Marketing

What are some of the most impactful experiences during your time at UC?

Every experience at UC has had a positive impact, helping me grow both personally and professionally. On campus employment has had a remarkable influence on my time at UC. From being a dining room assistant at MarketPointe dining center, to a student assistant at UC's leather research laboratory, to an office consultant at the Office of College Computing and to my current role of graduate assistant. Each position has imparted invaluable lessons on me. 

At UC, I have had the privilege of working with the best groups. Being a curious learner, I learn something new every day. Research wise, I have had the opportunity to present my research findings at reputable conferences where I've received feedback that has played a pivotal role in honing my presentation skills. I feel fortunate to have had the opportunity to work with supportive and understanding advisers and colleagues. 

What are a few accomplishments of which you are most proud?

Being part of two diverse research groups has enabled me to showcase my work to the world through multiple journal publications and conference presentations. For me, doing what I believe in every day is a major accomplishment. Receiving the Outstanding MS Thesis Award for my work on Sodium Phosphate Glasses and being named Graduate Student Engineer of the Month are accomplishments of mine. Additionally, I am proud to have been recognized by the International Journal of Applied Glass Science for contributing to their top cited article in 2021-2022. 

Finally, there is a sense of pride every time I see an article on semiconductor research in UC News and I am featured in it. Yes, it's me! I'm the guy in the cleanroom suit in those UC News articles. 

When do you expect to graduate? What are your plans after earning your degree?

I aim to graduate in either the summer or fall of 2024. Following that, I plan to apply the skills I've acquired during my time at UC to make a modest contribution to the ever-expanding field of semiconductors by working in one of the leading and top tier semiconductor organizations. 

Do you have any other hobbies, experiences or group involvements you'd like to share?

Outside of my research commitments, I am particularly interested in cooking, hiking, exploring new places, and photography. Additionally, I keep myself informed about the latest events and advancements in cricket, tennis and combat sports. I like working out at the campus recreation center and embrace opportunities to stay active whenever possible. In moments when there are no ongoing events in my preferred sports, I take the chance to explore and understand the intricacies of a new sport. 

I also serve as the president of the Electrical Engineering and Computer Science Graduate Student Association while also assisting the Graduate Student Government by being part of different committees. 

Featured image at top: Vamshi Kiran Gogi pictured at the Crater Lake in Oregon. Photo/Provided

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Material Characterization and Evolution through Laser Mater Interaction

March 8 @ 1:30 pm - 2:30 pm, event navigation, nirmala kandadai, phd, assistant professor | electrical and computer engineering | oregon state university.

Lasers have advanced a lot over the last 50 years since their first inception. They have a wide variety of applications based on their focused intensity from optical communication to generating high-energy particles. At Oregon State University, our Fiber optics and Integrated photonics laboratory uses laser-based techniques to understand the photo physics of materials that are distinctly different from their mechanical or electrical property. We utilize the photo physics of material to both characterize and manipulate the materials. In this talk, I will focus on our work on laser-material interaction using laser systems with focused intensities ranging from 10 5  W/cm 2  -10 15 W/cm 2 . We will show how each range of intensity has a distinct application, from laser sintering, and thermal conductivity measurement to the generation of 2D materials and nanoparticles.

Dr. Nirmala Kandadai  is an Assistant Professor in the Department of Electrical and Computer Engineering at Oregon State State University. She completed her Ph.D. at The University of Texas at Austin in 2012 studying the interaction of high-intensity laser with molecular gas clusters After her Ph.D., she worked for a year as a postdoctoral fellow at The University of Texas at Austin working on improving the contrast of a Petawatt laser system and then 3 years as a Laser Scientist at National energetics. At National Energetics, she led her team in designing and building high power ultrafast laser systems, including the front end of a 10 PW laser system for the European Union’s Extreme Light Infrastructure Beamlines facility (ELI-Beamlines). She was in Boise as a Research Assistant Professor in 2016 and became a tenure track Assistant professor in 2019. She moved to Oregon State in 2022 as an Assistant Professor. At Oregon State she is the director of fiber optics laser and integrated research lab (FLAIR), her current research work includes laser mater interactions, sensors for extreme environments, infrared thermography, thermal conductivity, laser sintering, and plasma modeling.  She is currently an IEEE senior member.

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    Our PhD Program. Working with faculty who are leaders in the field, our PhD students conduct cutting-edge research, earning prestigious fellowships and awards . After graduation, they contribute widely to science, learning, culture, and their communities, earning honors in academia and industry. (They also throw rubber chickens at each other.)

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    Brown's data science master's program educates students in the methods and algorithms of data science, through a study of relevant topics in mathematics, statistics and computer science, including machine learning, data mining, visualization, and data management. The program also provides experience in important, frontline data science ...

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  20. Biostatistics

    All PhD graduate students are provided with a new laptop computer and office space. Students also have access to the computing infrastructure at the Center for Statistical Sciences, a high-end, continuously updated computing environment featuring both Unix and PC/MAC networks, with access to all major software for data analysis and numerical ...

  21. Computer Science Undergraduate Concentration

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  24. Brown launches fully online master's degree in biostatistics

    PROVIDENCE, R.I. [Brown University] — To serve a broad audience of learners and meet a pressing need for well-trained biostatistics professionals across the globe, Brown University is launching a fully online biostatistics master's program. Designed and developed by the Brown University School of Public Health and the School of Professional Studies, this 20-month degree program aims to ...

  25. Brown University

    The Brown University School of Public Health is accredited by the Council on Education in Public Health (CEPH). Like all CEPH-accredited programs, Brown's 100% online Biostatistics master's will teach and assess standard foundational competencies of public health, as well as health data science-specific competencies in analysis, leadership ...

  26. Engineering doctoral student leads cutting-edge semiconductor research

    Vamshi Kiran Gogi always wanted to be an engineer. During the first semester of his master's program at the University of Cincinnati, he developed a passion for semiconductor research, leading him to transition into a PhD program. Throughout his years as a Bearcat, Gogi has served as the president of the Electrical Engineering and Computer Science Graduate Student Association, trained students ...

  27. Material Characterization and Evolution through Laser Mater Interaction

    Dr. Nirmala Kandadai is an Assistant Professor in the Department of Electrical and Computer Engineering at Oregon State State University. She completed her Ph.D. at The University of Texas at Austin in 2012 studying the interaction of high-intensity laser with molecular gas clusters After her Ph.D., she worked for a year as a postdoctoral ...