yale university phd computer science

Computer Science

Undergraduate program, director of undergraduate studies.

http://dus.cs.yale.edu/

As is standard for STEM majors at Yale, Computer Science majors should meet with the DUS or the designated faculty representatives to receive academic advising. For Computer Science, there are designated advisors for each class that perform this role. Pointers to the DUS and class advisors can be found at https://cpsc.yale.edu/academics . If you are having difficulty reaching your class advisors, the DUS can act as a backup for advice.

The office hours or contact methods of professors serving as advisors for Fall 2023 are listed below.

• Mondays/Wednesdays 3:00-4:00 PM online ( https://yale.zoom.us/my/yryang ) or at AKW 208A
• Please send email (yry AT cs DOT yale DOT edu) to request appointments if need alternative time.
• Wednesdays 11:00-12 PM online ( https://yale.zoom.us/my/smitakrishnaswamy )
• Please send email (smita DOT krishnaswamy AT yale DOT edu) to request appointments if need alternative time or meet in person.
• Wednesdays 4:00-6:00 PM in AKW 212 or online ( https://yale.zoom.us/j/92476677132 )
• Tuesdays 11:00-12:00 PM online ( https://zoom.us/my/wibisono )
• Tuesdays 4:00-5:00 PM online ( https://yale.zoom.us/j/8699066849 ). Please book appointments using https://calendly.com/yang-cai/csjunior
• Please send email to make appointments.
• Tuesdays 3:00pm-4:00 PM
• Please book appointment through https://calendly.com/marynelv/csjunior
• Mondays 3:00-4:00 PM
• AKW 204, or Zoom https://yale.zoom.us/j/4787418288
• Wednesdays 3:00-4:00 PM
• Room 325, 17 Hillhouse. Alternatively, students may also join via Zoom: https://yale.zoom.us/my/alexwong

Majors in Computer Science and Mathematics, Computer Science and Psychology, and Electrical Engineering and Computer Science should have their schedules signed by both the DUS in Computer Science (or the appropriate class advisors) and by the DUS of the other department represented in their major.

Majors in Computer Science and Economics and Computing in the Arts should contact the DUS for those programs instead.

The Department of Computer Science offers both B.S. and B.A. degree programs, as well as four combined majors in cooperation with other departments: Electrical Engineering and Computer Science, Computer Science and Economics, Computer Science and Mathematics, and Computer Science and Psychology. Each major program not only provides a solid technical education but also allows students either to take a broad range of courses in other disciplines or to complete the requirements of a second major.

The Computer Science and combined major programs share a common core of five computer science courses. The first is CPSC 201, a survey that demonstrates the breadth and depth of the field to students who have taken the equivalent of an introductory programming course. The remaining core courses cover discrete mathematics, data structures, systems programming and computer architecture, and algorithm analysis and design. Together these courses include the material that every major should know.

The core courses are supplemented by electives (and, for the combined majors, core courses in the other discipline) that offer great flexibility in tailoring a program to each student’s interests. The capstone is the senior project, through which students experience the challenges and rewards of original research under the guidance of a faculty mentor.

Prospective majors are encouraged to discuss their programs with the director of undergraduate studies as early as possible.  

Certificate in Programming

Coordinator: ted kim.

The Certificate in Programming prepares undergraduates to program computers in support of work in any area of study.  While the Certificate does not provide the same grounding in theory and systems that the computer science majors do, it does provide a short path to programming literacy that can be completed in a span of four terms.

Graduate Program

Director of graduate studies, lin zhong.

The Department offers two graduate programs: a doctoral program leading to a Doctor of Philosophy (Ph.D.) degree, and a terminal master’s program leading to a Master of Science (M.S.) degree. The doctoral program is intended for students preparing for a career in teaching and/or research.The terminal Master’s degree program is intended for students who want advanced study in computer science but do not intend to go on for the Ph.D. A student may apply to either the doctoral program or to the terminal master’s program. A student seeking the Ph.D. should apply directly to the doctoral program, even though he or she intends to obtain a Master’s degree along the way. A student who has completed the Master’s program and decides to go on for a Ph.D. is not guaranteed admission to the doctoral program and must apply in the normal way.

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Computer Science

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• Summary of Requirements
• Certificate

Directors of undergraduate studies:   Y. Richard Yang , 432-6400, AKW 208A; cpsc.yale.edu

The Department of Computer Science offers both B.S. and B.A. degree programs, as well as four combined major programs in cooperation with other departments: Electrical Engineering and Computer Science , Computer Science and Economics , Computer Science and Mathematics , and Computer Science and Psychology . Each program not only provides a solid technical education in the core of computer science but also  allows students to take a broad range of courses in other disciplines that are an essential part of a liberal arts education.

Specifically, the Computer Science and combined major programs share a common core of five computer science courses. The first is CPSC 201 , a survey that demonstrates the breadth and depth of the field to students who have taken the equivalent of an introductory programming course. The remaining core courses cover discrete mathematics ( CPSC 202  or MATH 244 ), data structures ( CPSC 223 ), systems programming and computer architecture ( CPSC 323 ), and algorithm analysis and design ( CPSC 365  or  366 ). Only one of  CPSC 365  or  366  may be taken for major credit. Together these courses include the material that every major should know.

The core courses are supplemented by electives (and, for a combined major, core courses in the other discipline) that offer great flexibility in tailoring a program to each student's interests. The capstone is the senior project ( CPSC 490 ), through which students experience the challenges and rewards of original research under the guidance of a faculty adviser.

Prospective majors are encouraged to discuss their programs with the director of undergraduate studies (DUS) as early as possible.

Introductory Courses

The department offers a broad range of introductory courses to meet the needs of students with varying backgrounds and interests. Except for  CPSC 200  and  CPSC 201 , none assumes previous knowledge of computers.

• CPSC 100  is taught jointly with Harvard University and teaches students majoring in any subject area how to program a computer and solve problems. No prior programming experience is required. Students with previous programming experience should consider taking  CPSC 201  instead. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 110  teaches programming for humanities and social sciences using the Python programming language. No prior programming experience is required. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 112 teaches students majoring in any subject area how to program a computer and solve problems using the Java programming language. No prior programming experience is required. Students with previous programming experience should consider taking CPSC 201 instead. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 134  provides an introduction to computer music, including musical representations for computing, automated music analysis and composition, interactive systems, and virtual instrument design.
• CPSC 150 explores how some of the key ideas in computer science have affected philosophy of mind, cognitivism, connectionism, and related areas. This humanities-style course requires a significant amount of reading and writing a paper, and satisfies the Writing and the Humanities and Arts distributional requirements.
• CPSC 151 studies the history of the graphical user interface in an attempt to guess its future. This course satisfies the Writing distributional requirement.
• CPSC 175 studies the C programming language and the Linux operating system. This course satisfies the Quantitative Reasoning requirement.
• CPSC 183 explores the myriad ways that law and technology intersect, with a special focus on the role of cyberspace. This course satisfies the Social Sciences distributional requirement.
• CPSC 184  focuses on the evolving and oftentimes vexing intellectual property regime of the new digital age. This course satisfies the Social Sciences and the Humanities and Arts distributional requirements.
• CPSC 185  covers the evolution of various legal doctrines with and around technological development. This course satisfies the Social Sciences and the Writing distributional requirements.
• CPSC 200 , intended as a survey course for non-majors, focuses on practical applications of computing technology while examining topics including computer hardware, computer software, and related issues such as security and software engineering. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 201  teaches the basic concepts, techniques, and applications of computer science, including systems (computers and their languages) and theory (complexity and computability). Students with sufficient programming experience may elect CPSC 201 without taking CPSC 112 . (These courses meet at the same time so that students are easily able to change levels if necessary.) This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 202 presents the formal methods of reasoning and the concepts of discrete mathematics and linear algebra used in computer science and related disciplines. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 210  examines the political challenges wrought by massive increases in the power of computational and communication technologies and the potential for citizens and governments to harness those technologies to solve problems. This course satisfies the Social Sciences distributional requirement.

Requirements of the Major

The B.S. and the B.A. degree programs have the same required five core courses: CPSC 201 ; CPSC 202 or MATH 244 ; CPSC 223 ;  CPSC 323 ; and  CPSC 365  or 366 . 

B.S. degree program The B.S. degree program requires a total of twelve term courses: five core courses, six intermediate or advanced courses in Computer Science, and the senior requirement.

B.A. degree program The B.A. degree program requires a total of ten term courses: the five core courses, four intermediate or advanced courses in Computer Science, and the senior requirement.

Combined B.S./M.S. degree Exceptionally able and well-prepared students may complete a course of study leading to the simultaneous award of the B.S. and M.S. degrees after eight terms of enrollment. General eligibility requirements are described in the Academic Regulations, section L, Special Academic Arrangements , “Simultaneous Award of the Bachelor's and Master's Degrees.” Specific requirements for the combined degree in Computer Science are as follows:

• Candidates must satisfy the Yale College requirements for the B.S. degree in Computer Science.
• At the end of their fifth term of enrollment, candidates must have earned at least nine of their Computer Science required course credits, which together with three additional Computer Science required course credits, satisfy the requirements for the B.S. in Computer Science. Candidates must also have achieved A grades (only A, not A-) in at least three-quarters of these courses.
• Candidates must also complete eight graduate courses from the approved list, up to two of which may, with the permission of the DUS and the director of graduate studies, also be applied toward completion of the B.S. degree. At most one of these eight courses may be CPSC 692 . All eight graduate courses must be completed in the final four terms of enrollment, and at least six of them must be completed in the final three terms of enrollment.

Credit/D/Fail Courses taken Credit/D/Fail may not be counted toward the major. All courses in the major must be taken for a letter grade.

Senior Requirement

In the senior year, students must take  CPSC 490 , an independent project course, in which a student selects an adviser to conduct original research with substantial work in a subfield of computer science. With permission of the DUS, students may enroll in  490  more than once or before their senior year.

All Computer Science majors in the sophomore, junior, and senior years should review their programs with their class advisers and the DUS. Students majoring in Computer Science are advised to complete CPSC 201 and 223 by the end of their sophomore year.

Electives  The field of computer science has broadened substantially in the last few decades and the Computer Science department advises its majors to choose intermediate and advanced electives covering the breadth of computer science, including theoretical computer science; computer systems and languages (e.g., database, networking, operating systems, programming languages, and systems security); and computer applications (e.g., artificial intelligence, computer graphics, computer vision, human-computer interactions, machine learning, natural language processing, and robotics).

The Computer Science department encourages interdisciplinary study in which computer science plays a major role. Advanced courses in other departments that involve concepts from computer science and are relevant to an individual program may, with permission of the DUS, be counted toward the requirements, but no more than two such courses may be counted toward the B.S., and no more than one toward the B.A.

Students interested in using computers to solve scientific and engineering problems are advised to take  CPSC 440  as well as computational courses offered in  Applied Mathematics  and in  Engineering and Applied Science .

The core mathematical background necessary to complete the Computer Science major is provided in  CPSC 202 . However, many advanced courses in graphics, computer vision, neural networks, and numerical analysis assume additional knowledge of linear algebra and calculus. Students who plan to take such courses as electives and who are unsure whether they have the appropriate mathematical background are encouraged to take  MATH 222  or  225 , MATH 226 , and MATH 120 .

Typical programs For students who already know how to program, typical B.S. programs starting in the first and sophomore years are indicated below. For typical B.A. programs, two of the electives would be omitted.

SUMMARY OF MAJOR REQUIREMENTS

Prerequisites None

Number of courses B.S. —12 term courses taken for letter grades (incl senior project); B.A. —10 term courses taken for letter grades (incl senior project)

Specific courses required B.S. and B.A. — CPSC 201 ; CPSC 202 or MATH 244 ; CPSC 223 ; CPSC 323 ; and  CPSC 365  or 366 . 

Distribution of courses B.S. —6 addtl intermediate or advanced Comp Sci courses; B.A. —4 addtl intermediate or advanced Comp Sci courses

Substitution permitted Advanced courses in other depts, with DUS permission

Senior requirement Senior project ( CPSC 490 )

Requirements for the B.S. Degree 

12 courses (for 12 credits) taken for letter grades, including the senior project

• CPSC 202 or  MATH 244
• CPSC 365 or CPSC 366  
• 6 additional intermediate or advanced computer science courses

Requirements for the B.A. Degree 

10 courses (for 10 credits) taken for letter grades, including the senior project

• CPSC 365 or  CPSC 366
• 4 additional intermediate or advanced computer science courses

The Computer Science department offers two degree programs, B.S. and B.A., and combined majors with Economics, Electrical Engineering, Mathematics, and Psychology. Each program provides a solid technical education yet allows students to take the broad range of courses in other disciplines that is an essential part of a liberal education.

The programs share a common core of five computer science courses, including CPSC 201 and courses in discrete mathematics, data structures, systems programming and computer architecture, and algorithm analysis and design. This core is supplemented by electives and, for the combined majors, core courses in the other discipline. The capstone of the major is the senior project, in which students conduct original research under the guidance of a faculty mentor.

Prospective majors are encouraged to discuss their program with the director of undergraduate studies (DUS) as early as possible.

The department offers a broad range of introductory courses for first-year students with varying backgrounds and interests. Except for CPSC 200 and CPSC 201 , none assumes previous knowledge of computers.

• CPSC 100  is taught jointly with Harvard University, and teaches students majoring in any subject area how to program a computer and solve problems. No prior programming experience is required. Students with previous programming experience should consider taking  CPSC 201 instead. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 110 teaches programming for humanities and social sciences using the Python programming language.   No prior programming experience is required. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 112  teaches students majoring in any subject area how to program a computer and solve problems using the Java programming language. No prior programming experience is required. Students with previous programming experience should consider taking CPSC 201  instead. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 150  explores how some of the key ideas in computer science have affected philosophy of mind, cognitivism, connectionism, and related areas. This humanities-style course requires a significant amount of reading and writing a paper, and satisfies the Writing and the Humanities and Arts distributional requirements.
• CPSC 175  studies the C programming language and the Linux operating system. This course satisfies the Quantitative Reasoning requirement.
• CPSC 183  explores the myriad ways that law and technology intersect, with a special focus on the role of cyberspace. This course satisfies the Social Sciences distributional requirement.
• CPSC 184 focuses on the evolving and oftentimes vexing intellectual property regime of the new digital age. This course satisfies the Social Sciences and the Humanities and Arts distributional requirements.
• CPSC 185 covers the evolution of various legal doctrines with and around technological development. This course satisfies the Social Sciences and the Writing distributional requirements.
• CPSC 201  teaches the basic concepts, techniques, and applications of computer science, including systems (computers and their languages) and theory (complexity and computability). Students with sufficient programming experience may elect  CPSC 201  without taking CPSC 112 . (These courses meet at the same time so that students are easily able to change levels if necessary.) This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 202  presents the formal methods of reasoning and the concepts of discrete mathematics and linear algebra used in computer science and related disciplines. This course satisfies the Quantitative Reasoning distributional requirement.
• CPSC 210 examines the political challenges wrought by massive increases in the power of computational and communication technologies and the potential for citizens and governments to harness those technologies to solve problems. This course satisfies the Social Science distributional requirement.

Certificate in Programming

Certificate in programming advisor:   T heodore Kim , AKW 412;  cpsc.yale.edu

The Certificate in Programming prepares students to program computers in support of work in any area of study. While the certificate does not provide the grounding in theory and systems that the computer science majors do, it does provide a short path to programming literacy that can be completed in a span of four terms. Majors in Computer Science, and in the joint programs with Economics, Electrical Engineering, Mathematics, and Psychology, or in Computing and the Arts may not pursue the Certificate. 

Refer to the  C omputer Science website for more information.

Prerequisite

The prerequisite for the Certificate is an introductory programming course, CPSC 100 ,  110 , 112 , S115 or successful completion of an AP Computer Science course. 

Requirements of the Certificate

Students may not use any of the five required courses, indicated below, to satisfy the requirements of any major or other certificate. If such a course is required for another program, the student must substitute another course from the same category or a more advanced one for the Programming Certificate. No course taken Credit/D/Fail may be used to satisfy any of the requirements; no course may be used to satisfy more than one of them.  

Programming  One from CPSC 201 or CPSC 200

Data structures   CPSC 223

Advanced programming  One from CPSC 327 or CPSC 323

A programming elective  A CPSC course with CPSC 223 as a listed or implied prerequisite and a primary focus on programming (such as CPSC 421 ,  422 , 424 , 433 , 434 ,  437 , 439 , 446 , or 478 ) or a second course that satisfies the advanced programming requirement

An applications or algorithms elective   Either programming in context course that requires significant programming (such as CPSC 334 , 335 , 376 , 431 , 432 , 474 , 477 , 479 , or LING 380 ) or a course in algorithms (such as CPSC 365 or 366 )  

Theodore Kim from the Department of Computer Science is the Certificate Coordinator. He advises students pursuing the Certificate. Exceptions to the requirements, other than the substitution of a more advanced course for a required one, are limited. 

Summary of Requirements  

Prerequisite   CPSC 100 , 110 , 112 , S115 or AP Computer Science course

Number of courses  5 term courses 

Specific courses required   CPSC 201 or 200 ; CPSC 223 ; CPSC 327 or 323

Distribution of courses   2 electives, as specified

FACULTY OF THE DEPARTMENT OF COMPUTER SCIENCE

Professors  Dana Angluin ( Emeritus ), James Aspnes, *Dirk Bergemann, Abhishek Bhattacharjee, Julie Dorsey, Joan Feigenbaum, Michael Fischer, David Gelernter, *Mark Gerstein, Theodore Kim, †Vladimir Rokhlin, Holly Rushmeier ( Chair ), Brian Scassellati, Martin Schultz (Emeritus), Zhong Shao, Avi Silberschatz, †Daniel Spielman, Nisheeth Vishnoi, Y. Richard Yang ( DUS ), Lin Zhong ( DGS ), †Steven Zucker

Associate Professors Yang Cai, Smita Krishnaswamy, Charalampos Papamanthou, Ruzica Piskac, Robert Soulé

Assistant Professors  *Kim Blenman, Arman Cohan, Yongshan Ding, Benjamin Fisch, Tesca Fitzgerald, Anurag Khandelwal, Quanquan Liu, Daniel Rakita, Katerina Sotiraki, Marynel Vázquez, Andre Wibisono, Alex Wong, Rex Ying, Manolis Zampetakis, Fan Zhang

Senior Research Scientists  Robert Bjornson, Andrew Sherman

Senior Lecturers  James Glenn, Scott Petersen, Stephen Slade

Lecturers  Timothy Barron, Andrew Bridy, Xiuye (Sue) Chen, Ozan Erat, Jay Lim, Dylan McKay, Cody Murphey, Sohee Park, Brad Rosen, Inyoung Shin, Alan Weide, Cecillia Xie 

*A secondary appointment with a primary affiliation in another department or school.

†A joint appointment with primary affiliation in another department or school.

For a complete list of Computer Science Department personnel, visit the  department website .

See visual roadmap of the requirements.

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Computing and the arts, you are here.

The C2 initiative has both undergraduate and graduate components.

At the Undergraduate level, Yale College students may choose to major in “Computing and the Arts.”  The major requires 14 Yale College courses, including two project courses that satisfy the Yale senior requirement.  Seven of the courses are in computer science, and seven are from one of the art discipline tracks.  The required courses are specified to provide students with a sound grounding in the fundamentals of computing and one of the arts.  The two term project offers the student the opportunity to synthesize the fundamentals in an independent study performed with guidance from both computing and the arts faculty members.  Outside of the formal course requirements, students are offered the opportunity to participate in ongoing computing and the arts related research.

At the graduate level, there are opportunities at the MS and PhD levels within the Computer Science Department.  The terminal MS degree in Computer Science with a Computing and the Arts track requires 7 courses and a faculty guided independent project.  The PhD program provides the opportunity to pursue in-depth state-of-the-art research in computing and the arts after satisfying the common requirements from the PhD in Computer Science.

Graduate Study

• Advanced Graduate Leadership Program
• Forms and Guides

Graduate Degrees

Ph.d. degree.

The online publication Qualification Procedure for the Ph.D. Degree in Engineering & Applied Science describes in detail all requirements in Biomedical Engineering, Chemical & Environmental Engineering, Electrical Engineering, and Mechanical Engineering & Materials Science. The student is strongly encouraged to read it carefully; key requirements are briefly summarized below. See Computer Science's departmental entry in this bulletin for special requirements for the Ph.D. in Computer Science.

Students plan their course of study in consultation with faculty advisers (the student's advisory committee). A minimum of ten term courses is required, to be completed in the first two years. Well-prepared students may petition for course waivers based on courses taken in a previous graduate degree program. Similarly, students may place out of certain ENAS courses via an examination prepared by the course instructor. Placing out of the course will not reduce the total number of required courses. Core courses, as identified by each department/program, should be taken in the first year unless otherwise noted by the department. With the permission of the departmental director of graduate studies (DGS), students may substitute more advanced courses that cover the same topics. No more than two courses can be Special Investigations, and at least two must be outside the area of the dissertation. All students must complete a one-term course, Responsible Conduct of Research, in the first year of study. Information on graduate courses offered in ENAS can be found at https://courses.yale.edu/ .

Each term, the faculty review the overall performance of the student and report their findings to the DGS who, in consultation with the associate dean, determines whether the student may continue toward the Ph.D. degree. By the end of the second term, it is expected that a faculty member has agreed to accept the student as a research assistant. By December 5 of the third year, an area examination must be passed and a written prospectus submitted before dissertation research is begun. These events result in the student's admission to candidacy. Subsequently, the student will report orally each year to the full advisory committee on progress. When the research is nearing completion, but before the thesis writing has commenced, the full advisory committee will advise the student on the thesis plan. A final oral presentation of the dissertation research is required to be given during term time. There is no foreign language requirement.

Teaching experience is regarded as an integral part of the graduate training program at Yale University, and all Engineering graduate students are required to serve as a Teaching Fellow for up to two terms, typically during year two. Teaching duties normally involve assisting in laboratories or discussion sections and grading papers and are not expected to require more than ten hours per week. Students are not permitted to teach during the first year of study.

If a student was admitted to the program having earned a score of less than 26 on the Speaking Section of the Internet-based TOEFL, the student will be required to take an English as a Second Language (ESL) course each term at Yale until the Graduate School's Oral English Proficiency standard has been met. This must be achieved by the end of the third year in order for the student to remain in good standing.

Doctoral students who are accepted to our program usually receive financial support (tuition and stipend) for their entire period of study, provided their performance is satisfactory.

A unique feature of our doctoral program is support during the first year from University Fellowships; this financial independence gives beginning graduate students the freedom to explore various topics with different faculty members. For the 2020-2021 academic year, the tuition plus stipend amounts to $82,450 (tuition: $45,700, stipend: $36,750). Exceptional students receive financial supplements from the School of Engineering & Applied Science in addition to their University Fellowship.

After their first year, students are usually appointed Assistants in Research, and their tuition support and stipend come from the grants and contracts of their faculty research advisors.

M.D./Ph.D. Degree

M.D./Ph.D. students affiliate with the Department of Biomedical Engineering via the Medical School. M.D./Ph.D. students officially affiliate with Biomedical Engineering after selecting a thesis adviser and consulting with the director of graduate studies (DGS).

The academic requirements for M.D./Ph.D. students entering Biomedical Engineering are modified from the normal requirements for Ph.D. students. Other than the modifications listed here, M.D./Ph.D. students in Biomedical Engineering are subject to all of the same requirements as the other graduate students in the department.

Courses: Seven graduate-level courses taken for a grade must be completed during the first two years of the Ph.D. program. (One Yale graduate-level course taken for a grade during medical school may be counted toward this requirement at the discretion of the DGS.) There are three required courses: ENAS 510 and two semesters of ENAS 990. All students are expected to present their Special Investigation work at a department symposium held on the last day of the reading period. In addition, there is a math requirement, which may be met by taking any one of the following courses: ENAS 500, ENAS 505, ENAS 549. Among the three electives, one must be in engineering or a closely related field. Students must obtain a grade of Honors in any two of these courses, excluding ENAS 990, and maintain an average of at least High Pass.

Teaching: Students are required to serve as a teaching fellow for up to two terms but are not permitted to teach during their first year of graduate study.

Prospectus and Qualifying exam: M.D./Ph.D. students must complete and submit their thesis prospectus by the end of the fifth semester as an affiliated graduate student. If the student affiliates at the customary point of year three, they must submit the approved prospectus before the end of the fall semester of the fifth year (at the beginning of year three as an affiliated Ph.D. student). After submitting the prospectus, students present their results to date and their proposed research to their thesis committee in an Area Examination. Students are given two opportunities to pass this exam.

Candidacy: M.D./Ph.D. students will be admitted to candidacy once they have completed their course requirements, passed their qualifying exam, and had their dissertation prospectus approved by their advisory committee.

Further requirements: M.D./Ph.D. students who are admitted to candidacy are required to have an annual Thesis Committee meeting. In the first year after admission to candidacy, students are expected to present their research work at a departmental seminar. Attendance at weekly Biomedical Engineering Seminars is mandatory. A final oral presentation of the dissertation research is required before students may submit to the Dissertation Office.

Master's Degrees

M.Phil. The Master of Philosophy is awarded en route to the Ph.D. in SEAS. The minimum general requirements for this degree are that a student shall have completed all requirements for the Ph.D. except required teaching, the prospectus, and dissertation. Students will not generally have satisfied the requirements for the Master of Philosophy until after two years of study, except where graduate work done before admission to Yale has reduced the student's graduate course work at Yale. In no case will the degree be awarded for less than one year of residence in the Yale Graduate School.

M.S. (en route to the Ph.D.): To qualify for the M.S., the student must pass eight term courses; no more than two may be Special Investigations. An average grade of at least High Pass is required, with at least one grade of Honors.

Terminal Master's Degree Program: Students may also be admitted directly to a terminal master's degree program in Engineering & Applied Science. The requirements are the same as for the M.S. en route to the Ph.D., although there are no core course requirements for students in this program. This program is normally completed in one year, but a part-time program may be spread over as many as four years. Some courses are available in the evening, to suit the needs of students from local industry.

The Master’s of Science in Personalized Medicine & Applied Engineering is a program directed and taught jointly by faculty in the School of Engineering & Applied Science and the School of Medicine. The program is intended to prepare biomedical, mechanical, and electrical engineers, as well as computer science majors and medical students, with the tools to develop innovative 3D solutions for personalized medicine.  The advancement of our understanding of complex medical conditions--together with the advent of high-resolution medical imaging, 3D printing, robotics, computer navigation, extended, virtual and augmented reality--offers an opportunity to develop custom treatments, patient-specific instruments for surgery and personalized medical devices. This degree program will train graduate students to develop and apply 3D technology to address surgical and medical conditions, with the goal of personalizing healthcare treatments to improve patient clinical outcomes. Additional societal benefits include lower healthcare costs (increased efficiency, lower complications, increased collaboration, improved sustainability) and improved patient quality of life. Prospective students should apply through the Graduate School of Arts and Sciences and more information about the degree can be found here .

The program is one full year: summer through spring.  Students are required to participate in an eight-week, summer clinical immersion session prior to registration in fall semester sequence courses. Although course credit is not awarded for the clinical program, completion of the requirement will be noted on the transcript.

Course Requirements:   Given that the program will attract students from many different backgrounds, students will have flexibility in selecting the focus of their special investigation projects as well as an optional biomedical engineering industry collaboration project (“internal internship”). For example, students with a strong engineering background may want to focus on medical school-focused classes, while medical students may want to focus on engineering-related courses. In order to graduate, students will need to take a total of eight courses, of which six courses are required and two may be chosen from Yale-wide graduate-level technical electives, which must be approved by the program’s DGS. An average grade of at least High Pass is required, with at least one grade of Honors.

The following six courses are required of all students in the program: ENAS 526, ENAS 527, ENAS 528, ENAS 529, and two semesters of ENAS 990.

Joint Master's Degree Program (School of Engineering & Applied Science and School of the Environment): The joint master's degree program offered by the School of the Environment (YSE) and the School of Engineering & Applied Science (SEAS) provides environmental engineers and environmental managers with the opportunity to develop knowledge and tools to address the complex relationship between technology and the environment. This joint-degree program will train graduate students to design and manage engineered and natural systems that address critical societal challenges, while considering the complex technical, economic, and sociopolitical systems relationships. Each joint program leads to the simultaneous award of two graduate professional degrees: either the Master of Environmental Management (M.E.M.) or the Master of Environmental Science (M.E.Sc.) from YSE, and a Master of Science (M.S.) from SEAS. Students can earn the two degrees concurrently in 2.5 years, less time than if they were pursued sequentially. Candidates spend the first year at YSE, the second year at SEAS, and their final term at YSE. Joint-degree students are guided in this process by advisers in both YSE and SEAS. Candidates must submit formal applications to both YSE and SEAS and be admitted separately to each School, i.e., each School makes its decision independently. It is highly recommended that students apply to and enter a joint-degree program from the outset, although it is possible to apply to the second program once matriculated at Yale. Prospective students to the joint-degree program apply to the YSE master's degree through YSE and to the SEAS master's degree in Chemical & Environmental Engineering through the Graduate School of Arts and Sciences .

The following six courses are required of all joint-degree YSE/SEAS master's students completing their M.S. in Environmental Engineering: ENAS 641, ENAS 642, ENAS 660, ENV 773, ENV 838, and either ENV 712 or ENV 724. Two additional Yale-wide technical electives approved by the DGS (or faculty in an equivalent role in Environmental Engineering) are required. These courses may be cross-listed with or administered by YSE with prior approval from the DGS. For the joint-degree requirements for completion of the M.E.M. or M.E.Sc. in YSE, see the bulletin of the Yale School of the Environment at https://bulletin.yale.edu .

To Perspective Students

Acknowledgement.

• Yale Profile
• selected publications

Rex (Zhitao) Ying

I'm currently an assistant professor in the department of computer science at Yale University.

I work on AI and machine learning algorithms that leverage graph structure of data.

I'm excited to be a founding member of Kumo.ai !

Welcome to my personal website

Refer to my Yale profile for more information. Our lab is hiring motivated and capable Ph.D students interested in geometric deep learning, graph neural nets, relational reasoning with foundation models, and trustworthy AI.

Marketing Director

Creative Executive

Tawana Cherry

Company President

Introduction

Prospective students.

Dr. Rex (Zhitao) Ying

Assistant professor

• It's our second year organizing the New Frontiers of Graph Learning Workshop at NeurIPS 2023 . Submissions and attendance are welcomed!
• I'm giving a talk at Stanford Graph Learning Workshop on "Deep learning for time series forecasting problems with relational information". Date: Oct 24th 2023.
• I'm giving a seminar talk at Duke University on "Graph Learning for intelligent and efficient reasoning". Date: Oct 2nd 2023.
• As KDD PhD Consortium Chair, I organized the 1-day PhD Consortium event at KDD 2023 .
• We had 4 papers published at ICML 2023 and KDD 2023.
• I gave a keynote at AAAI 2023 Deep Learning on Graphs: Methods and Applications Workshop on "Graph learning for non-graph data".
• I'm excited to be a founding engineer of Kumo.ai , where we work on building a cloud AI platform based on state-of-the-art GNNs and the PyG library.
• I'm the winner of KDD 2022 Dissertationn Award , and will present the thesis at the KDD 2022 Conference .
• I'm one of the 10 winners of the 2019 Baidu Scholarship in Artificial Intelligence.

Research Outline

• I work on advancing graph neural network (GNN) architectures that improve the expressiveness, scalability and interpretability of GNNs.
• I'm interested in empowering deep learning architectures with effective representation geometry, which is essential in modeling data manifolds with different characteristics. For example, we explore modeling of hierarchical relations through hyperbolic embeddings .
• I work on real-world applications in physical simulations, chemistry and biology predictions, knowledge graphs, natural languages, recommendations and social networks. What interest me the most are novel ways with which we can identify intrinsic connections within the data and empower machine learning algorithms by leveraging relational information.

Deep Learning for Graphs

Multimodal relational foundation models, geometric representation learning, unlimited applications, course websites.

• Deep Learning for Graph-Structured Data

My Past Workshops

• New Frontiers in Graph Learning ( GLFrontiers ) at NeurIPS 2022 and NeurIPS 2023
• Deep Learning for Simulation ( SimDL ) at ICLR 2021
• Stanford Graph Learning Workshop ( SGL )
• Graph Representationn Learning and Beyond ( GRL+ ) at ICML 2020
• Co-organized the 2020 KDD Cup Competition on Graph AutoML .
• WEB DESIGN 90%
• HTML5 CSS3 80%
• WordPress 75%

Selected Publications

Deep learning on graphs.

I focus on advancing graph neural network (GNN) architectures and improving the expressiveness, scalability, interpretability and robustness of GNNs. More recently, I focus on pre-trained, large-scale foundation models for graph-structured data.

Learning to Group Auxiliary Datasets for Molecule

DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting

BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs

Local Augmentation for Graph Neural Networks

Neural Execution of Graph Algorithms

Identity-aware Graph Neural Networks

​Inductive Representation Learning on Large Graphs

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

GNNExplainer: Generating Explanations for Graph Neural Networks

NeurIPS 2017

NeurIPS 2019

Representation Learning

I innovate in representation learning techniques and embedding geometry for embedding for data with different characteristics (hierarchical, heterogeneous etc.).

Hyperbolic Representation Learning: Revisiting and Advancing

Hyperbolic Graph Topic Modeling Network with Continuously Updated Topic Tree

Hyperbolic Graph Convolutional Neural Networks

Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones

Neural Distance Embeddings for Biological Sequences

NeurIPS 2021

Representation Learning on Graphs: Methods and Applications

Hierarchical Graph Representation Learning with Differentiable Pooling

Position-aware Graph Neural Networks

IEEE Data Engineering Bulletin 2017

NeurIPS 2018

Applications

Natural phenomenon and world's knowledge can often be expressed with the language of graphs. In addition to the popular applications of graphs such as social networks, recommender systems, knowledge graphs, biological networks and molecules , I'm also interested in novel ways of incorporating relational reasoning to other fields of science and technology, such as physical simulations, natural language and industrial relational database predictions .

Get in touch

Learning to Simulate Complex Physics with Graph Networks

Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator

Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

Bipartite Dynamic Representations for Abuse Detection

• Early reachout and collaboration with my lab can be a very effective way to stand out from all the candidates.
• Highly competitive PhD student applicants usually had abundant research experiences prior to the application. Note that the number of publications is not the crucial factor.
• Student who had a single publication, but demonstrated outstanding ability (usually as a first author) in idea formulation, implementation, experiments, analysis etc. is considered more competitive than a student who participated in many research works but did not own / lead one from beginning to the end.
• Publications in top-tier ML and data mining conferences such as NeurIPS, ICML, ICLR, KDD, WebConf etc. are highly encouraged. High-impact journal publications in interdisciplinary fields are also highly appreciated.
• It is recommended that the field of your prior research is under the broad category of machine learning. However, the actual research topic does not need to be similar to mine, as long as the candidate demonstrates interests and understanding of the research topics of our lab. We welcome diversity at all levels, including skill sets!

Note (special focus):

• If you are interested in applying for PhD program focusing on AI for neuroscience, explicitly mention it in the email when reaching out to me. I encourage you to check out WTI (computational track) , which I'm a part of.
• If you are interested in AI for computational biology, check out Yale CBB program , which I'm also a part of.
• Successful candidates usually have 3 or more solid and impactful publications in an area, and have a coherent and unified thesis on a specific topic, encompassing a number of works.
• Similar to evaluating PhD applicants, I value paper quality over quantity.
• Prior experiences in leading a large-scope project will be appreciated.
• The candidates are required to have extensive research experiences in either foundation models, graph learning, trustworthy deep learning or relational reasoning.

Visiting Students

Organizations.

Copyright © 2022 Rex (Zhitao) Ying

10 PhD Students Named 2023-24 Prize Teaching Fellows

Ten PhD students from the Graduate School of Arts and Sciences (GSAS) have been named Prize Teaching Fellows for the 2023-2024 academic year: Camille Angelo (Religious Studies), Carissa Chan (Microbiology), Grayson Hoy (Chemistry), Nghiem Huynh (Economics), Kimberly Lifton (Medieval Studies), Benjamin Schafer (History), Jillian Stallman (Economics), Audrey Tjahjadi (Anthropology), Alexa Williams (Chemistry), and Novak Yang (Immunobiology). 

The Graduate School has awarded the teaching prizes annually since 2000. Recipients are nominated by their undergraduate students and the faculty members they assist while serving as Teaching Fellows.

"Doctoral education is more than just a journey from knowledge acquisition to knowledge creation," said Lynn Cooley, Dean of the Graduate School. "It is fundamentally about equipping scholars with the ability to share their insights broadly—to impact society positively through education. Reviewing the nominations, I am profoundly impressed by the innovative and engaging ways in which our teaching fellows have made complex ideas accessible and exciting to their students."

Biographies of the winners are included below.

Camille Leon Angelo (Religious Studies)

Camille Leon Angelo is a sixth-year PhD student in the Department of Religious Studies in the subfields of Eastern Mediterranean and West Asian Religions and Ancient Christianity. Her work examines materiality, sexuality, and space in late antiquity through new materialist, feminist, and queer lenses. She is a field archaeologist and has excavated in the eastern Mediterranean and the Caucasus. Her current research primarily engages archaeological, papyrological, and epigraphic evidence, related to late antique Egypt. Her past projects have analyzed the archaeological remains of several early Christian sites in the eastern Mediterranean and North Africa, most notably Dura-Europos, to elucidate sensory experiences in late antiquity.

Carissa Chan (Microbiology)

Carissa Chan is a fifth-year PhD candidate in Microbiology. Her research investigates how bacterial pathogens adapt to infection-relevant stresses, thus promoting survival inside mammalian host cells and disease. She has served as a teaching fellow for Physiological Systems for the past three years, including two as head teaching fellow. Each year, Carissa is inspired by the dedication and level of engagement from students in the class as they cover fascinating topics about the human body from fundamental cellular physiology to complex interactions between organ systems. Working with undergraduate and graduate students in Physiological Systems and sharing her excitement for science with them has been one of the highlights of her time at Yale.

Grayson Hoy (Chemistry)

Grayson Hoy is a first-year PhD student in the Chemistry Department. His research focuses on using super-resolution infrared microscopy to study metabolism in living cells to better understand metabolic dysregulation. Before Yale, he attended William & Mary, where he learned how transformative professors and mentors can be from a student’s perspective. Inspired by his undergraduate researcher professor, Dr. Kristin Wustholz, and other teachers throughout his life, Grayson aims to create a supportive learning environment where students feel empowered and excited by chemistry. 

Nghiem Huynh (Economics)

Nghiem Huynh is a doctoral candidate in Economics at Yale University, graduating in May 2024. His research evaluates the effects of government policies on regional and gender inequality. Nghiem holds a BA in Economics and Mathematics from New York University Abu Dhabi.

Kimberly Lifton (Medieval Studies)

Kimberly Lifton is a PhD candidate in the Medieval Studies program. She studies how Burgundy, England, and France's relationships with the Ottoman Empire materialized in manuscripts during the fifteenth century. Her research has been supported by the Fulbright, FLAS, and the Dhira Mahoney Fellowship. In the classroom, she works to develop compassionate pedagogy for neurodiverse students. 

Benjamin Schafer (History)

Benjamin Schafer is a PhD candidate in American History. He studies urban and social history in the late-twentieth-century United States.  His dissertation, “Life and Death in Rust,” is a study of poverty and inequality in post-industrial Buffalo, NY, his hometown, from the late 1970s to the early 2000s. Prior to Yale, Ben received an AB, magna cum laude with highest honors; Phi Beta Kappa, in History with a secondary in African American Studies from Harvard College, where he was awarded the Thomas T. Hoopes Senior Thesis Prize, the David Herbert Donald Prize in American History, and the Rev. Peter J. Gomes Prize in Religion and Ethnicity. He also holds an MPhil in Economic and Social History from Emmanuel College, University of Cambridge. He works as a research assistant for Professors Elizabeth Hinton and Vanessa Ogle and has previously worked as a researcher for Professor Fredrik Logevall (Harvard) and the John F. Kennedy Library Foundation. He has been a teaching fellow for Professor David Engerman (Fall 2023, The Origins of U.S. Global Power) and Professor Marco Ramos (Spring 2023, The History of Drugs in America).

Jillian Stallman (Economics)

Jillian Stallman is a PhD student in the Economics Department interested broadly in the intersection of economic development, environmental economics, and political economy. She's writing her dissertation about cooperation over freshwater resources in developing countries using a combination of economic theory, surveys and administrative data, and remote-sensing measurements. Jillian spent her undergraduate years at Williams College, where she worked most semesters as a teaching assistant to her peers in courses ranging from macroeconomic development to multivariable calculus to introductory Chinese. After graduating, she spent several years travelling in, among other places, China, Chile and Senegal, operating under the belief that she would have a difficult time ultimately doing research about places and people she hadn't lived around for a good while.

Audrey Tjahjadi (Anthropology)

Audrey Tjahjadi is a third-year PhD student in the Department of Anthropology focusing on human evolutionary genetics. She is interested in how local environments have shaped the evolution of diet-related adaptations in Southeast Asian and Oceanic populations, particularly in genes involved in fatty acid metabolism. Outside of research, Audrey is also involved in science communication and outreach through Yale graduate student organizations. 

Alexa Kim Williams (Chemistry)

Alexa Williams is a PhD student in Materials Chemistry. She completed her BS in Chemistry in 2021 at Montclair State University in New Jersey. At Yale, her research explores the fundamental reactivity of H-terminated silicon nanoparticles and aims to inform broader studies on silicon-based hybrid materials for CO2 reduction. This work is part of the CHASE solar fuels hub.

Xuan (Novak) Yang (Immunobiology)

Novak Yang is a third-year PhD candidate in Dr. Lieping Chen’s laboratory at the Department of Immunobiology. He received his BS in Biology and MS in Cancer Biology and Translational Oncology degrees at Emory University, and was the first to accomplish this in a “3+1” timeline at Emory. Prior to joining Yale, Novak was trained by Dr. Haian Fu and Dr. Andrey Ivanov at the Department of Pharmacology and Chemical Biology, Emory University School of Medicine, with a primary focus on cancer-associated protein-protein interactions and high-throughput drug discovery. He has multiple first-author and co-author publications, and is the recipient of American Society for Pharmacology and Experimental Therapeutics (ASPET) Travel Award and Program Committee Blue Ribbon Pick, and Society for Laboratory Automation and Screening (SLAS) Tony B. Academic Travel Award. Novak was recruited to Yale Immunobiology in 2021 as a Gruber Science Fellow. His research focuses on the discovery of actionable targets in the tumor microenvironment that drive the resistance to current immunotherapies, and pre-clinical development of innovative therapeutic strategies that normalize anti-tumor immunity for cancer patients.