## Results for CS |
90 courses |

A practical introduction to using the Unix operating system with a focus on Linux command line skills. Class will consist of video tutorials and weekly hands-on lab sections. Topics include: grep and regular expressions, ZSH, Vim and Emacs, basic and advanced GDB features, permissions, working with the file system, revision control, Unix utilities, environment customization, and using Python for shell scripts. Topics may be added, given sufficient interest. Course website: http://cs1u.stanford.edu

Terms: Aut, Win, Spr
| Units: 1

This course is about the fundamentals and contemporary usage of the Python programming language. The primary focus is on developing best practices in writing Python and exploring the extensible and unique parts of the Python language. Topics include: Pythonic conventions, data structures such as list comprehensions, anonymous functions, iterables, powerful built-ins (e.g. map, filter, zip), and Python libraries. For the last few weeks, students will work with course staff to develop their own significant Python project. Prerequisite: CS106B, CS106X, or equivalent.

Terms: Spr
| Units: 2

Instructors: ; Cain, J. (PI); Cooper, M. (PI)

Continuation of CS51 (CS + Social Good Studio). Teams enter the quarter having completed and tested a minimal viable product (MVP) with a well-defined target user, and a community partner. Students will learn to apply scalable technical frameworks, methods to measure social impact, tools for deployment, user acquisition techniques and growth/exit strategies. The purpose of the class is to facilitate students to build a sustainable infrastructure around their product idea. CS52 will host mentors, guest speakers and industry experts for various workshops and coaching-sessions. The class culminates in a showcase where students share their projects with stakeholders and the public. Prerequisite: CS 51, or consent of instructor.

Terms: Spr
| Units: 2

Instructors: ; Cain, J. (PI)

Additional problem solving practice for the introductory CS course CS 106A. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106A required.

Terms: Aut, Win, Spr
| Units: 1

Additional problem solving practice for the introductory CS course CS106B. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106B required.

Terms: Aut, Win, Spr
| Units: 1

What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in CS103A. Prerequisite: CS106B or equivalent. CS106B may be taken concurrently with CS103.

Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-FR

For non-technical majors. What computers are and how they work. Practical experience in programming. Construction of computer programs and basic design techniques. A survey of Internet technology and the basics of computer hardware. Students in technical fields and students looking to acquire programming skills should take 106A or 106X. Students with prior computer science experience at the level of 106 or above require consent of instructor. Prerequisite: minimal math skills.

Terms: Aut, Spr
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR

Introduction to the engineering of computer applications emphasizing modern software engineering principles: program design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the built-in facilities of respective languages. Uses the Python programming language. No prior programming experience required.

Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent.

Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR

A follow up class to CS106A for non-majors which will both provide practical web programming skills and cover essential computing topics including computer security and privacy. Additional topics will include digital representation of images and music, an exploration of how the Internet works, and a look at the internals of the computer. Students taking the course for 4 units will be required to carry out supplementary programming assignments in addition to the course's regular assignments. Prerequisite: 106A or equivalent

Terms: Spr
| Units: 3-4

Instructors: ; Young, P. (PI)

Supplemental lab to 106B and 106X. Additional features of standard C++ programming practice. Possible topics include advanced C++ language features, standard libraries, STL containers and algorithms, templates, object memory management, operator overloading, and move semantics. Prerequisite: consent of instructor. Corequisite: CS106B or CS106X.

Terms: Aut, Win, Spr
| Units: 1

Instructors: ; Schwarz, K. (PI); Zelenski, J. (PI)

Survey course on applications of fundamental computer science concepts from CS 106B/X to problems in the social good space (such as health, government, education, and environment). Each week consists of in-class activities designed by student groups, local tech companies, and nonprofits. Introduces students to JavaScript and the basics of web development. Some of the topics we will cover include mental health chatbots, tumor classification with basic machine learning, sentiment analysis of tweets on refugees, and storytelling through virtual reality. Pre/Corequisite: CS106B or CS106X.

Terms: Aut, Win, Spr
| Units: 1

Introduction to the fundamental concepts of computer systems. Explores how computer systems execute programs and manipulate data, working from the C programming language down to the microprocessor. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, memory organization and management, and performance evaluation and optimization. Prerequisites: 106B or X, or consent of instructor.

Terms: Aut, Win, Spr
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR

Additional problem solving practice for the introductory CS course CS107. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 107 required.

Terms: Aut, Win, Spr
| Units: 1

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of MATH 51 or CME 100 or equivalent.

Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

Principles and practice of engineering of computer software and hardware systems. Topics include: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities. Prerequisite: 107.

Terms: Aut, Win, Spr
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci

Explores operating system concepts including concurrency, synchronization, scheduling, processes, virtual memory, I/O, file systems, and protection. Available as a substitute for CS110 that fulfills any requirement satisfied by CS110. Prerequisite: CS107.

Terms: Spr
| Units: 3-5

Instructors: ; Mazieres, D. (PI); Ousterhout, J. (PI)

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS106B

Terms: Spr
| Units: 3-4
| UG Reqs: WAY-AQR

Instructors: ; Jurafsky, D. (PI)

Students will implement a simple, clean operating system (virtual memory, processes, file system) in the C programming language, on a rasberry pi computer and use the result to run a variety of devices and implement a final project. All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required.

Terms: Spr
| Units: 3-4

Instructors: ; Engler, D. (PI)

Concepts and techniques used in constructing interactive web applications. Browser-side web facilities such as HTML, cascading stylesheets, the document object model, and JavaScript frameworks and Server-side technologies such as server-side JavaScript, sessions, and object-oriented databases. Issues in web security and application scalability. New models of web application deployment. Prerequisite: CS 107.

Terms: Win, Spr
| Units: 3

Instructors: ; Rosenblum, M. (PI)

Principles and practices for design and implementation of compilers and interpreters. Topics: lexical analysis; parsing theory; symbol tables; type systems; scope; semantic analysis; intermediate representations; runtime environments; code generation; and basic program analysis and optimization. Students construct a compiler for a simple object-oriented language during course programming projects. Prerequisites: 103 or 103B, and 107.

Terms: Spr
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci

Instructors: ; Kjoelstad, F. (PI)

Logic Programming is a style of programming based on symbolic logic. In writing a logic program, the programmer describes the application area of the program (as a set of logical sentences) without reference to the internal data structures or operations of the system executing the program. In this regard, a logic program is more of a specification than an implementation; and logic programs are often called runnable specifications. This course introduces basic logic programming theory, current technology, and examples of common applications, notably deductive databases, logical spreadsheets, enterprise management, computational law, and game playing. Work in the course takes the form of readings and exercises, weekly programming assignments, and a term-long project. Prerequisite: CS 106B or equivalent.

Terms: Spr
| Units: 3

Instructors: ; Genesereth, M. (PI)

For seniors and first-year graduate students. Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system security, application security (web, apps, databases), malware, privacy, and security for mobile devices. Course projects focus on building reliable code. Prerequisite: 110. Recommended: basic Unix.

Terms: Spr
| Units: 3
| UG Reqs: GER:DB-EngrAppSci

Instructors: ; Boneh, D. (PI); Durumeric, Z. (PI)

This course is designed as a deep dive into the design, analysis, implementation, and theory of data structures. Over the course of the quarter, we'll explore fundamental techniques in data structure design (isometries, amortization, randomization, word-level parallelism, etc.). In doing so, we'll see a number of classic data structures like Fibonacci heaps and suffix trees as well as more modern data structures like count-min sketches and range minimum queries. By the time we've finished, we'll have seen some truly beautiful strategies for solving problems efficiently. Prerequisites: CS107 and CS161.

Terms: Spr
| Units: 3-4

Instructors: ; Schwarz, K. (PI)

This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS107 and CS161, or permission from the instructor.

Terms: Spr
| Units: 3-4

Instructors: ; Valiant, G. (PI)

This project-based course aims to bring together students from computer science and the social sciences to work with external partner organizations at the nexus of digital technology and public policy. Students will collaborate in interdisciplinary teams on a problem with a partner organization. Along with the guidance of faculty mentors and the teaching staff, students will engage in a project with outcomes ranging from policy memos and white papers to data visualizations and software. Possible projects suggested by partner organizations will be presented at an information session in early March. Following the infosession, a course application will open for teams to be selected before the start of Spring Quarter. Students may apply to a project with a partner organization or with a preformed team and their own idea to be reviewed for approval by the course staff. There will be one meeting per week for the full class and at least one weekly meeting with the project-based team mentors. Prerequisites: Appropriate preparation depends on the nature of the project proposed, and will be verified by the teaching staff based on your application.

Terms: Spr
| Units: 3

Instructors: ; Ullman, J. (PI)

Restricted to Computer Science students. Group or individual projects under faculty direction. Register using instructor's section number. A project can be either a significant software application or publishable research. Software application projects include substantial programming and modern user-interface technologies and are comparable in scale to shareware programs or commercial applications. Research projects may result in a paper publishable in an academic journal or presentable at a conference. Public presentation of final application or research results is required. Prerequisite: Completion of at least 135 units and consent of instructor. Project proposal form is required before the beginning of the quarter of enrollment: https://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdf

Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Angst, R. (PI); Bailis, P. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Borenstein, J. (PI); Boyd, S. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Gregg, C. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Icard, T. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Lee, C. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Ma, T. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Niebles Duque, J. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Pande, V. (PI); Parlante, N. (PI); Pea, R. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Poldrack, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubin, D. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Saxena, A. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Sosic, R. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Wang, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wootters, M. (PI); Wu, J. (PI); Yamins, D. (PI); Yeung, S. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

Restricted to Computer Science students. Writing-intensive version of CS191. Register using instructor's section number. Prerequisite: Completion of at least 135 units and consent of instructor. Project proposal form is required before the beginning of the quarter of enrollment: https://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdf

Terms: Aut, Win, Spr
| Units: 3-6
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Borenstein, J. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Gregg, C. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Icard, T. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Niebles Duque, J. (PI); Okamura, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Pande, V. (PI); Parlante, N. (PI); Pea, R. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubin, D. (PI); Saberi, A. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Saxena, A. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Wang, G. (PI); Wetzstein, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wodtke, C. (PI); Wu, J. (PI); Yeung, S. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

Restricted to Computer Science students. Appropriate academic credit (without financial support) is given for volunteer computer programming work of public benefit and educational value. Register using the section number associated with the instructor. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-4
| Repeatable
for credit

Instructors: ; Aiken, A. (PI); Altman, R. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Boneh, D. (PI); Cain, J. (PI); Cao, P. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Gregg, C. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Wu, J. (PI); Young, P. (PI); Zelenski, J. (PI)

Build mobile applications using tools and APIs in iOS. Developing applications for the iPhone and iPad requires integration of numerous concepts including functional programming, object-oriented programming, computer-human interfaces, graphics, animation, reactive interfaces, Model-View-Intent (MVI) and Model-View-View-Model (MVVM) design paradigms, object-oriented databases, networking, and interactive performance considerations including multi-threading. This course will require you to learn a new programming language (Swift) as well as a new-to-iOS development environment, SwiftUI. Prerequisites: All coursework (homework and final project) involves writing code, so writing a lot of code should not be ¿new¿ to you (coding experience in almost any language is valuable, but object-oriented (e.g. CS108) and/or functional programming languages (e.g. CS43) are most highly recommended). CS106A and B (or X) and CS107 (or equivalent) are hard prerequisites. Any other courses that help to develop your maturity as a programmer are also recommended.

Terms: Spr
| Units: 3

Instructors: ; Hegarty, P. (PI)

Design, specification, coding, and testing of a significant team programming project under faculty supervision. Documentation includes capture of project rationale, design and discussion of key performance indicators, a weekly progress log and a software architecture diagram. Public demonstration of the project at the end of the quarter. Preference given to seniors. May be repeated for credit. Prerequisites: CS 110 and CS 161.

Terms: Win, Spr
| Units: 3
| Repeatable
for credit

Instructors: ; Borenstein, J. (PI)

Restricted to Computer Science and Electrical Engineering undergraduates. Writing-intensive version of CS194. Preference given to seniors.

Terms: Win, Spr
| Units: 3

Instructors: ; Borenstein, J. (PI)

Directed research under faculty supervision. Register using instructor's section number. Students are required to submit a written report and give a public presentation on their work. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 3-4
| Repeatable
20 times
(up to 100 units total)

Instructors: ; Aiken, A. (PI); Barrett, C. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Chang, M. (PI); Charikar, M. (PI); Dror, R. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Finn, C. (PI); Fox, J. (PI); Genesereth, M. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Kundaje, A. (PI); Landay, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Li, F. (PI); Mitchell, J. (PI); Ng, A. (PI); Niebles Duque, J. (PI); Piech, C. (PI); Re, C. (PI); Savarese, S. (PI); Trippel, C. (PI); Troccoli, N. (PI); Valiant, G. (PI); Wodtke, C. (PI); Wu, J. (PI); Yamins, D. (PI)

An onramp for students interested in breaking new ground in the frontiers of computer science. Students select a research area (AI, HCI, Systems, etc.), and are matched with a quarter-long project and a Ph.D. student mentor. Lectures by faculty introduce the fundamentals of computer science research; special interest group meetings provide peer mentorship and feedback. Alumni of the course are given the opportunity to be connected to faculty for ongoing research, or to repeat the class under CS197A for credit (but no lecture component) to continue work on their projects. Prerequisites: Enrollment is by application. CS106B is required; CS107 is strongly recommended. Team projects will involve programming.

Terms: Spr
| Units: 4

Instructors: ; Yan, L. (PI)

Students lead a discussion section of 106A while learning how to teach a programming language at the introductory level. Focus is on teaching skills, techniques, and course specifics. Application and interview required; see http://cs198.stanford.edu.

Terms: Aut, Win, Spr
| Units: 3-4

Instructors: ; Eng, K. (PI); McCoy, E. (PI); Rydberg, K. (PI); Sahami, M. (PI); Tessier-Lavigne, E. (PI)

Students build on the teaching skills developed in CS198. Focus is on techniques used to teach topics covered in CS106B. Prerequisite: successful completion of CS198.

Terms: Aut, Win, Spr
| Units: 1

Instructors: ; Eng, K. (PI); McCoy, E. (PI); Rydberg, K. (PI); Sahami, M. (PI); Tessier-Lavigne, E. (PI)

Special study under faculty direction, usually leading to a written report. Register using instructor's section number. Letter grade; if not appropriate, enroll in CS199P. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Bailis, P. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Borenstein, J. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Chang, M. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Ganguli, S. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goodman, N. (PI); Grimes, A. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Icard, T. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Lee, C. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Lin, H. (PI); Liu, K. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Niebles Duque, J. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Pande, V. (PI); Parlante, N. (PI); Patrignani, M. (PI); Pavone, M. (PI); Pea, R. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubin, D. (PI); Rubinstein, A. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Saxena, A. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Stanford, J. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wodtke, C. (PI); Wootters, M. (PI); Wu, J. (PI); Yamins, D. (PI); Yan, L. (PI); Yeung, S. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); AbuHashem, A. (TA); Tchapmi P., L. (TA)

Special study under faculty direction, usually leading to a written report. Register using instructor's section number. CR/NC only, if not appropriate, enroll in CS199. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Angst, R. (PI); Barrett, C. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Borenstein, J. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Durumeric, Z. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goodman, N. (PI); Grimes, A. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Lee, C. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Lin, H. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Pavone, M. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Saxena, A. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Socher, R. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wodtke, C. (PI); Wootters, M. (PI); Wu, J. (PI); Yan, L. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

Businesses are built on ideas. Today¿s successful companies are those that most effectively generate, protect, and exploit new and valuable business ideas. Over the past 40 years, ¿intellectual capital¿ has emerged as the leading assets class. Ocean Tomo® estimates that over 80% of the market value of S&P 500 corporations now stems from ¿intangible¿ assets, which consist largely of intellectual property (IP) assets (e.g., the company and product names, logos and designs; patentable inventions; proprietary software and databases, and other proprietary product, manufacturing and marketing information). It is therefore vital for entrepreneurs and other business professionals to have a basic understanding of IP and how it is procured, protected, and exploited. This course provides an overview of the many and varied IP issues that students will confront during their careers. It is intended to be both informative and fun. Classes will cover the basics of patent, trademark, copyright, and trade secret law. Current issues in these areas will be covered, including patent protection for software and business methods, copyrightability of computer programs and APIs, issues relating to artificial intelligence, and the evolving protection for trademarks and trade secrets. Emerging issues concerning the federal Computer Fraud & Abuse Act (CFAA) and ¿hacking¿ will be covered, as will employment issues, including employee proprietary information and invention assignment agreements, work made for hire agreements, confidentiality agreements, non-compete agreements and other potential post-employment restrictions. Recent notable lawsuits will be discussed, including Apple v. Samsung (patents), Alice Corp. v. CLS Bank (software and business method patents), Oracle v. Google (software/APIs), Waymo v. Uber (civil and criminal trade secret theft), and hiQ v. LinkedIn (CFAA). IP law evolves constantly and new headline cases that arise during the term are added to the class discussion. Guest lectures typically include experts on open source software; legal and practical issues confronted by business founders; and, consulting and testifying as an expert in IP litigation. Although many of the issues discussed will involve technology disputes, the course also covers IP issues relating to art, music, photography, and literature. Classes are presented in an open discussion format and they are designed to be enjoyed by students of all backgrounds and areas of expertise.

Terms: Aut, Spr
| Units: 1

Instructors: ; Hansen, D. (PI)

Computational Law is an innovative approach to legal informatics concerned with the representation of regulations in computable form. From a practical perspective, Computational Law is important as the basis for computer systems capable of performing useful legal calculations, such as compliance checking, legal planning, and regulatory analysis. In this course, we look at the theory of Computational Law, we review relevant technology and applications, we discuss the prospects and problems of Computational Law, and we examine its philosophical and legal implications. Work in the course consists of reading, class discussion, and practical exercises.

Terms: Spr
| Units: 2-3

Instructors: ; Genesereth, M. (PI); Vogl, R. (PI)

Human decision making is increasingly being displaced by predictive algorithms. Judges sentence defendants based on statistical risk scores; regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. This course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fair systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises. Admission is by consent of instructor and is limited to 20 students. To enroll in the class, please complete the course application by March 20, available at: https://5harad.com/mse330/. Grading is based on response papers, class participation, and a final project. Prerequisite: CS 106A or equivalent knowledge of coding.

Terms: Spr
| Units: 3

Instructors: ; Goel, S. (PI)

Continuation of CS210A. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: CS 210A

Terms: Spr
| Units: 3-4

Instructors: ; Borenstein, J. (PI)

Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.

Terms: Aut, Win, Spr
| Units: 3-4

Instructors: ; Anari, N. (PI); Finn, C. (PI); Hashimoto, T. (PI); Liang, P. (PI); Sadigh, D. (PI); Wu, J. (PI); Hong, F. (TA); Jones, E. (TA); Kim, B. (TA); Koh, P. (TA); Kondrich, A. (TA); Kuck, J. (TA); Lam, G. (TA); Lettiere, A. (TA); Li, V. (TA); Palsson, M. (TA); Raghunathan, A. (TA); Sawhney, A. (TA); Soylu, D. (TA); Wang, W. (TA); Zhang, Y. (TA)

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180/280, CS 124, CS 224N, or CS 224S.

Terms: Spr
| Units: 3-4

Instructors: ; MacCartney, B. (PI); Potts, C. (PI)

A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.

Terms: Spr
| Units: 3

Instructors: ; Genesereth, M. (PI)

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/numpy, familiarity with probability theory to the equivalency of CS109 or STATS116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51.

Terms: Aut, Spr, Sum
| Units: 3-4

Instructors: ; Charikar, M. (PI); Ma, T. (PI); Ng, A. (PI); Re, C. (PI); Caron, P. (TA); Ding, T. (TA); Do, D. (TA); Fuster, A. (TA); Jain, S. (TA); Kamalu, J. (TA); Li, H. (TA); Nie, X. (TA); Shu, R. (TA); Sun, A. (TA); Waites, C. (TA); Wolff, C. (TA); Yuan, H. (TA); Z. HaoChen, J. (TA); Zhu, M. (TA)

Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications). CS 229 may be taken concurrently.

Terms: Aut, Win, Spr
| Units: 3-4
| UG Reqs: WAY-AQR, WAY-FR

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks. Prerequisites: Proficiency in Python; CS131 and CS229 or equivalents; MATH21 or equivalent, linear algebra.

Terms: Spr
| Units: 3-4

Instructors: ; Li, F. (PI)

Mathematical and computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces -- as well as for other data embedded into geometric spaces. Global and local geometry descriptors allowing for various kinds of invariances. The rudiments of computational topology and persistent homology on sampled spaces. Clustering and other unsupervised techniques. Spectral methods for graph data. Linear and non-linear dimensionality reduction techniques. Alignment, matching, and map computation between geometric data sets. Function spaces and functional maps. Networks of data sets and joint analysis for segmentation and labeling. Deep learning on irregular geometric data. Prerequisites: discrete algorithms at the level of CS161; linear algebra at the level of Math51 or CME103.

Terms: Spr
| Units: 3

Instructors: ; Guibas, L. (PI)

The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab or Python highly recommended.

Terms: Spr
| Units: 3-4

Recent research. Classic and new papers. Topics: virtual memory management, synchronization and communication, file systems, protection and security, operating system extension techniques, fault tolerance, and the history and experience of systems programming. Prerequisite: 140 or equivalent.

Terms: Spr
| Units: 3
| Repeatable
for credit

Instructors: ; Engler, D. (PI)

Classic papers, new ideas, and research papers in networking. Architectural principles: why the Internet was designed this way? Congestion control. Wireless and mobility; software-defined networks (SDN) and network virtualization; content distribution networks; packet switching; data-center networks. Prerequisite: 144 or equivalent.

Terms: Spr
| Units: 3-4

Instructors: ; Katti, S. (PI); McKeown, N. (PI)

The availability of massive datasets is revolutionizing science and industry. This course discusses data mining and machine learning algorithms for analyzing very large amounts of data. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality-sensitive hashing, shingling, min-hashing); Stream data processing; Recommender Systems; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (decision tree ensembles); Multi-armed bandit; Computational advertising. Prerequisites: At least one of CS107 or CS145.

Terms: Spr
| Units: 3-4
| UG Reqs: WAY-FR

Instructors: ; Leskovec, J. (PI)

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? How do you create responsible, ethical, human centered experiences? Let us explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: CS147 or equivalent background in design thinking.

Terms: Aut, Spr
| Units: 3-4

Instructors: ; Stanford, J. (PI)

Principles of web security. The fundamentals and state-of-the-art in web security. Attacks and countermeasures. Topics include: the browser security model, web app vulnerabilities, injection, denial-of-service, TLS attacks, privacy, fingerprinting, same-origin policy, cross site scripting, authentication, JavaScript security, emerging threats, defense-in-depth, and techniques for writing secure code. Course projects include writing security exploits, defending insecure web apps, and implementing emerging web standards. Prerequisite: CS 142 or equivalent web development experience.

Terms: Spr
| Units: 3

Instructors: ; Aboukhadijeh, F. (PI)

A continuation of CS254 (Computational Complexity). Topics include Barriers to P versus NP; The relationship between time and space, and time-space tradeoffs for SAT; The hardness versus randomness paradigm; Average-case complexity; Fine-grained complexity; Current and new areas of complexity theory research. Prerequisite: CS254.

Terms: Spr
| Units: 3

Instructors: ; Tan, L. (PI)

Many 21st-century computer science applications require the design of software or systems that interact with multiple self-interested participants. This course will provide students with the vocabulary and modeling tools to reason about such design problems. Emphasis will be on understanding basic economic and game theoretic concepts that are relevant across many application domains, and on case studies that demonstrate how to apply these concepts to real-world design problems. Topics include auction and contest design, equilibrium analysis, cryptocurrencies, design of networks and network protocols, reputation systems, social choice, and social network analysis. Case studies include BGP routing, Bitcoin, eBay's reputation system, Facebook's advertising mechanism, Mechanical Turk, and dynamic pricing in Uber/Lyft. Prerequisites: CS106B/X and CS161, or permission from the instructor.

Terms: Spr
| Units: 3

Instructors: ; Rubinstein, A. (PI)

Capstone Biomedical Informatics (BMI) experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.

Terms: Spr
| Units: 3-5

Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python and R.

Terms: Spr
| Units: 3

Leveraging off three synchronized sets of symbolic data resources for notation and analysis, the lab portion introduces students to the open-source Humdrum Toolkit for music representation and analysis. Issues of data content and quality as well as methods of information retrieval, visualization, and summarization are considered in class. Grading based primarily on student projects. Prerequisite: 253 or consent of instructor.

Terms: Spr
| Units: 2-4

Instructors: ; Sapp, C. (PI); Selfridge-Field, E. (PI)

Today we interact with our friends and enemies, our team partners and romantic partners, and our organizations and societies, all through computational systems. How do we design these social computing systems to be effective and responsible? This course covers design patterns for social computing and crowdsourcing systems, and the foundational ideas that underpin them. Students will engage in the creation of new computationally-mediated social environments.

Terms: Spr
| Units: 3

Instructors: ; Bernstein, M. (PI)

Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Prerequisite: consent of instructor.

Terms: Spr
| Units: 1

Instructors: ; Gregg, C. (PI)

Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Prerequisites: 223A or equivalent.

Terms: Spr
| Units: 3

Instructors: ; Khatib, O. (PI)

The progress of machine learning systems has seemed remarkable and inexorable ¿ a wide array of benchmark tasks including image classification, speech recognition, and question answering have seen consistent and substantial accuracy gains year on year. However, these same models are known to fail consistently on atypical examples and domains not contained within the training data. The goal of the course is to introduce the variety of areas in which distributional shifts appear, as well as provide theoretical characterization and learning bounds for distribution shifts. Prerequisites: CS229 or equivalent. Recommended: CS229T (or basic knowledge of learning theory).

Terms: Spr
| Units: 3

Instructors: ; Hashimoto, T. (PI)

A representation performs the task of converting an observation in the real world (e.g. an image, a recorded speech signal, a word in a sentence) into a mathematical form (e.g. a vector). This mathematical form is then used by subsequent steps (e.g. a classifier) to produce the outcome, such as classifying an image or recognizing a spoken word. Forming the proper representation for a task is an essential problem in modern AI. In this course, we focus on 1) establishing why representations matter, 2) classical and moderns methods of forming representations in Computer Vision, 3) methods of analyzing and probing representations, 4) portraying the future landscape of representations with generic and comprehensive AI/vision systems over the horizon, and finally 5) going beyond computer vision by talking about non-visual representations, such as the ones used in NLP or neuroscience. The course will heavily feature systems based on deep learning and convolutional neural networks. We will have several teaching lectures, a number of prominent external guest speakers, as well as presentations by the students on recent papers and their projects. nnRequired Prerequisites: CS131A, CS231A, CS231B, or CS231N. If you do not have the required prerequisites, please contact a member of the course staff before enrolling in this course.

Terms: Spr
| Units: 3

Instructors: ; Savarese, S. (PI)

Deep learning-based AI systems have demonstrated remarkable learning capabilities. A growing field in deep learning research focuses on improving the Fairness, Accountability, and Transparency (FAccT) of a model in addition to its performance. Although FAccT will be difficult to achieve, emerging technical approaches in this topic show promise in making better FAccT AI systems. In this course, we will study the rigorous computer science necessary foundations for FAccT deep learning and dive into the technical underpinnings of topics including fairness, robustness, interpretability, accountability, and privacy. These topics reflect state-of-the-art research in FAccT, are socially important, and they have strong industrial interest due to government and other policy regulation. This course will focus on the algorithmic and statistical methods needed to approach FAccT AI from a deep learning perspective. We will also discuss several application areas where we can apply these techniques. Prerequisites: Intermediate knowledge of statistics, machine learning, and AI. Qualified students will have taken any one of the following, or their advanced equivalents: CS224N, CS230, CS231N, CS236, CS273B. Alternatively, students who have taken CS229 or have equivalent knowledge can be admitted with the permission of the instructors.

Terms: Spr
| Units: 3

Instructors: ; Landay, J. (PI); Wei, W. (PI)

(Previously numbered CS376.) How will the future of human-computer interaction evolve? This course equips students with the major animating theories of human-computer interaction, and connects those theories to modern innovations in research. Major theories are drawn from interaction (e.g., tangible and ubiquitous computing), social computing (e.g., Johansen matrix), and design (e.g., reflective practitioner, wicked problems), and span domains such as AI+HCI (e.g., mixed initiative interaction), accessibility (e.g., ability based design), and interface software tools (e.g., threshold/ceiling diagrams). Students read and comment on multiple research papers per week, and perform a quarter-long research project. Prerequisites: For CS and Symbolic Systems undergraduates/masters students, CS147 or CS247. No prerequisite for PhD students or students outside of CS and Symbolic Systems.

Terms: Spr
| Units: 3-4
| Repeatable
for credit

Instructors: ; Abtahi, P. (PI); Metaxa, D. (PI)

Intermediate level, emphasizing high-quality image synthesis algorithms and systems issues in rendering. Topics include: Reyes and advanced rasterization, including motion blur and depth of field; ray tracing and physically based rendering; Monte Carlo algorithms for rendering, including direct illumination and global illumination; path tracing and photon mapping; surface reflection and light source models; volume rendering and subsurface scattering; SIMD and multi-core parallelism for rendering. Written assignments and programming projects. Prerequisite: 248 or equivalent. Recommended: Fourier analysis or digital signal processing.

Terms: Spr
| Units: 3-4

Instructors: ; Hanrahan, P. (PI)

This course introduces technologies and mathematical tools for simulating, modeling, and controlling human/animal movements. Students will be exposed to integrated knowledge and techniques across computer graphics, robotics, machine learning and biomechanics. The topics include numerical integration, 3D character modeling, keyframe animation, skinning/rigging, multi-body dynamics, human kinematics, muscle dynamics, trajectory optimization, learning policies for motor skills, and motion capture. Students who successfully complete this course will be able to use and modify physics simulator for character animation or robotic applications, to design/train control policies for locomotion or manipulation tasks on virtual agents, and to leverage motion capture data for synthesizing realistic virtual humans. The evaluation of this course is based on three assignments and an open-ended research project. Recommended Prerequisite: CS148 or CS205A

Terms: Spr
| Units: 3

Instructors: ; Liu, K. (PI)

Visual computing tasks such as computational photography, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large data centers. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that execute and accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. Students will perform daily research paper readings, complete simple programming assignments, and compete a self-selected term project. Prerequisites: CS 107 or equivalent. Highly recommended: Parallel Computing (CS149) or Computer Architecture (EE 282). Students will benefit from some background in deep learning (CS 230, CS 231N), computer vision (CS 231A), digital image processing (CS 232) or computer graphics (CS248).

Terms: Spr
| Units: 3-4

Instructors: ; Fatahalian, K. (PI)

This course explores the field of secure compilation, which sits at the intersection between security and programming languages. The course covers the following topics: threat models for secure compilers, formal criteria for secure compilers to adhere to, security relevance of secure compilation criteria, security architectures employed to achieve secure compilation, proof techniques for secure compilation with a focus on backtranslation.

Terms: Spr
| Units: 3

Instructors: ; Patrignani, M. (PI)

Coding theory is the study of how to encode data to protect it from noise. Coding theory touches CS, EE, math, and many other areas, and there are exciting open problems at all of these frontiers. In this class, we will explore these open problems by reading recent research papers and thinking about some open problems together. Required work will involve reading and presenting research papers, as well as working in small groups at these open problems and presenting progress. (Solving an open problem is not required!) Topics will depend on student interest and may include locality, coded computation, index coding, interactive communication, and group testing. Prerequisites: CS250 / EE387 or EE388; or linear algebra and permission of the instructor.

Terms: Spr
| Units: 3

Instructors: ; Wootters, M. (PI)

Topics: Pseudo randomness, multiparty computation, pairing-based and lattice-based cryptography, zero knowledge protocols, and new encryption and integrity paradigms. May be repeated for credit. Prerequisite: CS255.

Terms: Spr
| Units: 3
| Repeatable
for credit

Technology has enabled the emergence of economic systems of formerly inconceivable complexity. Nevertheless, some technology-related economic problems are so complex that either supercomputers cannot solve them in a reasonable time, or they are too complex for humans to comprehend. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. This topics course explores simplicity and complexity in economics, primarily via theoretical models. We will focus on recent advances. Key topics include (but are not limited to) resource allocation in complex environments, communication complexity and information aggregation in markets, robust mechanisms, dynamic matching theory, influence maximization in networks, and the design of simple (user-friendly) mechanisms. Some applications include paired kidney exchange, auctions for electricity and for radio spectrum, ride-sharing platforms, and the diffusion of information. Prerequisites: Econ 203 or equivalent.

Terms: Spr
| Units: 3-5

Design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles. Prerequisites: some familiarity with probability, programming, and multivariable calculus.

Terms: Spr
| Units: 3-4

Instructors: ; Kochenderfer, M. (PI)

Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks. However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago. This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, attention mechanisms for enabling Interpretability, meta learning, and privacy-preserving training data management. The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment, AI/VR for surgery, and health education) will feature guest speakers from academia and industry. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Example project topics are but not limited to 1) knowledge guided GANs for improving training data diversity, 2) disease diagnosis via multimodal symptom checking, and 3) fake and biased news/information detection.

Terms: Spr
| Units: 3

Instructors: ; Chang, E. (PI)

This class focuses on building agents that achieve human-level performance in specialized technical domains and are adept at collaborating with humans using natural language. We draw upon research in cognitive and systems neuroscience to take advantage of what is known about how humans communicate and solve problems in order to design advanced artificial neural network architectures. For more detail, see http://www.stanford.edu/class/cs379c/ with special attention to the CALENDAR and DISCUSSION tabs from past classes available by following the ARCHIVES link.

Terms: Spr
| Units: 3

Instructors: ; Dean, T. (PI)

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS390A, CS390B, and CS390C may each be taken once.

Terms: Aut, Win, Spr, Sum
| Units: 1

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Bailis, P. (PI); Barrett, C. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Borenstein, J. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Duchi, J. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fischer, M. (PI); Fisher, K. (PI); Follmer, S. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Gregg, C. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Icard, T. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kjoelstad, F. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Lee, C. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Liu, K. (PI); Ma, T. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Niebles Duque, J. (PI); Okamura, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Pande, V. (PI); Parlante, N. (PI); Pavone, M. (PI); Pea, R. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubinstein, A. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Sidford, A. (PI); Sosic, R. (PI); Stanford, J. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Troccoli, N. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Wetzstein, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wootters, M. (PI); Wu, J. (PI); Yamins, D. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS390A, CS390B, and CS390C may each be taken once.

Terms: Aut, Win, Spr, Sum
| Units: 1

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Bailis, P. (PI); Barrett, C. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Chang, M. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Duchi, J. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Follmer, S. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Gregg, C. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Icard, T. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kjoelstad, F. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Lee, C. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Ma, T. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Okamura, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Pavone, M. (PI); Pea, R. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubinstein, A. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Troccoli, N. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wodtke, C. (PI); Wootters, M. (PI); Wu, J. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS 390A, CS390B, and CS390C may each be taken once.

Terms: Aut, Win, Spr, Sum
| Units: 1

Instructors: ; Aiken, A. (PI); Altman, R. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Engler, D. (PI); Ermon, S. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kjoelstad, F. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Lee, C. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Pavone, M. (PI); Pea, R. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wu, J. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI)

For qualified computer science PhD students only. Permission number required for enrollment; see the CS PhD program administrator in Gates room 195. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science PhD students engage in research and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. Students on F1 visas should be aware that completing 12 or more months of full-time CPT will make them ineligible for Optional Practical Training (OPT).

Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Bailis, P. (PI); Barrett, C. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Duchi, J. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Follmer, S. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hayden, P. (PI); Hennessy, J. (PI); Horowitz, M. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Liu, K. (PI); Ma, T. (PI); Manning, C. (PI); Mazieres, D. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Pande, V. (PI); Parlante, N. (PI); Pavone, M. (PI); Piech, C. (PI); Plotkin, S. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubinstein, A. (PI); Saberi, A. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wootters, M. (PI); Wu, J. (PI); Yan, L. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

For CS graduate students. A substantial computer program is designed and implemented; written report required. Recommended as a preparation for dissertation research. Register using the section number associated with the instructor. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-9
| Repeatable
for credit

Instructors: ; Aiken, A. (PI); Altman, R. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Boneh, D. (PI); Cain, J. (PI); Cao, P. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Engler, D. (PI); Ermon, S. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI)

For graduate students in Computer Science. Use of database management or file systems for a substantial application or implementation of components of database management system. Written analysis and evaluation required. Register using the section number associated with the instructor. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit

Instructors: ; Aiken, A. (PI); Altman, R. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Boneh, D. (PI); Cain, J. (PI); Cao, P. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Engler, D. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Fisher, K. (PI); Fox, A. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Shoham, Y. (PI); Thrun, S. (PI); Tobagi, F. (PI); Ullman, J. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Young, P. (PI); Zelenski, J. (PI)

Letter grade only. This course is for masters students only. Undergraduate students should enroll in CS199; PhD students should enroll in CS499. Letter grade; if not appropriate, enroll in CS399P. Register using the section number associated with the instructor. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-9
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Barrett, C. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Borenstein, J. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Gregg, C. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Icard, T. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kjoelstad, F. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Lee, C. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Ma, T. (PI); MacCartney, B. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Montgomery, S. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Niebles Duque, J. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Pande, V. (PI); Parlante, N. (PI); Patrignani, M. (PI); Pea, R. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubin, D. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Saxena, A. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Sidford, A. (PI); Socher, R. (PI); Sosic, R. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Varodayan, D. (PI); Wang, G. (PI); Wetzstein, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wodtke, C. (PI); Wootters, M. (PI); Wu, J. (PI); Yamins, D. (PI); Yan, L. (PI); Yeung, S. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

Graded satisfactory/no credit. This course is for masters students only. Undergraduate students should enroll in CS199; PhD students should enroll in CS499. S/NC only; if not appropriate, enroll in CS399. Register using the section number associated with the instructor. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-9
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Bailis, P. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Boneh, D. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Engler, D. (PI); Ermon, S. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Lee, C. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Plotkin, S. (PI); Plummer, R. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Saxena, A. (PI); Shoham, Y. (PI); Socher, R. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Valiant, G. (PI); Van Roy, B. (PI); Varodayan, D. (PI); Wang, G. (PI); Wetzstein, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Wodtke, C. (PI); Wu, J. (PI); Yan, L. (PI); Yeung, S. (PI); Young, P. (PI); Zelenski, J. (PI)

An introductory, project-based exploration of systems and processes for making things using computer-aided design and manufacturing, and an introduction to machines and machine tools. Emphasis will be placed on building novel machines and related software for use by "makers" and interactive machines. Course projects will encourage students to understand, build and modify/hack a sequence of machines: (1) an embroidery machine for custom textiles, (2) a paper cutting machine (with drag knife) for ornamental design, and (3) an XY plotter with Arduino controller. Through these projects students explore both (i) principles of operation (mechanical, stepper motors and servos, electrical control, computer software), and (ii) computer algorithms (trajectory, tool path, design). Current trends in interactive machines will be surveyed. The course will culminate in a final student-selected project. Prerequisite: CS106A or equivalent programming experience. Students should have a desire to make things.

Terms: Spr
| Units: 3-4

Instructors: ; Hanrahan, P. (PI); James, D. (PI)

Letter grade only. Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS199, masters students should enroll in CS399. Letter grade; if not appropriate, enroll in CS499P.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Anari, N. (PI); Bailis, P. (PI); Barrett, C. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Bohg, J. (PI); Boneh, D. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Duchi, J. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Follmer, S. (PI); Fox, A. (PI); Fox, J. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Icard, T. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kjoelstad, F. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Liu, K. (PI); Ma, T. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Montanari, A. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Pavone, M. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubinstein, A. (PI); Saberi, A. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Saxena, A. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Sidford, A. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Utterback, C. (PI); Valiant, G. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wootters, M. (PI); Wu, J. (PI); Yamins, D. (PI); Yan, L. (PI); Yeung, S. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

Graded satisfactory/no credit. Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS199, masters students should enroll in CS399. S/NC only; if not appropriate, enroll in CS499.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Bailis, P. (PI); Barrett, C. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Duchi, J. (PI); Durumeric, Z. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Follmer, S. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Goodman, N. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); Icard, T. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kjoelstad, F. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Liu, K. (PI); Ma, T. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Mitra, S. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Paepcke, A. (PI); Parlante, N. (PI); Pavone, M. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubinstein, A. (PI); Saberi, A. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Saxena, A. (PI); Schwarz, K. (PI); Shoham, Y. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Utterback, C. (PI); Valiant, G. (PI); Van Roy, B. (PI); Wang, G. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wootters, M. (PI); Wu, J. (PI); Yamins, D. (PI); Yan, L. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)

Weekly speakers on human-computer interaction topics. May be repeated for credit.

Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit

Instructors: ; Bernstein, M. (PI); Mullings, C. (TA)

Terminal Graduate Registration (TGR). CS PhD students who have their TGR form approved should register under the section number associated with their faculty advisor.

Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit

Instructors: ; Agrawala, M. (PI); Aiken, A. (PI); Altman, R. (PI); Barrett, C. (PI); Batzoglou, S. (PI); Bejerano, G. (PI); Bernstein, M. (PI); Blikstein, P. (PI); Bohg, J. (PI); Boneh, D. (PI); Boyd, S. (PI); Brunskill, E. (PI); Cain, J. (PI); Cao, P. (PI); Charikar, M. (PI); Cheriton, D. (PI); Dally, B. (PI); Dill, D. (PI); Dror, R. (PI); Engler, D. (PI); Ermon, S. (PI); Fatahalian, K. (PI); Fedkiw, R. (PI); Feigenbaum, E. (PI); Fikes, R. (PI); Finn, C. (PI); Fisher, K. (PI); Fox, A. (PI); Fox, J. (PI); Garcia-Molina, H. (PI); Genesereth, M. (PI); Gill, J. (PI); Girod, B. (PI); Goel, A. (PI); Goel, S. (PI); Guibas, L. (PI); Hanrahan, P. (PI); Hashimoto, T. (PI); Hennessy, J. (PI); Horowitz, M. (PI); James, D. (PI); Johari, R. (PI); Jurafsky, D. (PI); Katti, S. (PI); Kay, M. (PI); Khatib, O. (PI); Kjoelstad, F. (PI); Kochenderfer, M. (PI); Koller, D. (PI); Kozyrakis, C. (PI); Kundaje, A. (PI); Lam, M. (PI); Landay, J. (PI); Latombe, J. (PI); Leskovec, J. (PI); Levis, P. (PI); Levitt, M. (PI); Levoy, M. (PI); Li, F. (PI); Liang, P. (PI); Liu, K. (PI); Ma, T. (PI); Manning, C. (PI); Mazieres, D. (PI); McCarthy, J. (PI); McKeown, N. (PI); Mitchell, J. (PI); Musen, M. (PI); Nayak, P. (PI); Ng, A. (PI); Olukotun, O. (PI); Ousterhout, J. (PI); Parlante, N. (PI); Pavone, M. (PI); Pea, R. (PI); Piech, C. (PI); Plotkin, S. (PI); Plummer, R. (PI); Potts, C. (PI); Prabhakar, B. (PI); Pratt, V. (PI); Raghavan, P. (PI); Rajaraman, A. (PI); Re, C. (PI); Reingold, O. (PI); Roberts, E. (PI); Rosenblum, M. (PI); Roughgarden, T. (PI); Rubinstein, A. (PI); Sadigh, D. (PI); Sahami, M. (PI); Salisbury, J. (PI); Savarese, S. (PI); Shoham, Y. (PI); Tan, L. (PI); Thrun, S. (PI); Tobagi, F. (PI); Trippel, C. (PI); Ullman, J. (PI); Utterback, C. (PI); Valiant, G. (PI); Van Roy, B. (PI); Widom, J. (PI); Wiederhold, G. (PI); Winograd, T. (PI); Winstein, K. (PI); Wootters, M. (PI); Wu, J. (PI); Young, P. (PI); Zaharia, M. (PI); Zelenski, J. (PI); Zou, J. (PI)