nyu phd computer science

PhD in Computer Science

The Tandon School of Engineering offers a PhD in Computer Science. Cybersecurity is a particular research strength of the program. Learn more and apply to the PhD in Computer Science  through the Tandon School of Engineering.

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Computer Science

Degrees and fields of study.

• Computer Science (Non-Degree)
• Preparatory Accelerated Courses (PAC) Sequence - Suspended for Fall 2024
• M.S.  in Computer Science
• M.S.  in Computing, Entrepreneurship and Innovation
• M.S.  in Information Systems
• Refer to Mathematics for requirements 
• Ph.D.  in Computer Science
• Ph.D.-J.D.  in Computer Science/Law (Dual Degree)

Application Deadlines

Applications and all supporting materials must be  submitted online by 5PM  Eastern Time. If a listed deadline falls on a Saturday, Sunday, or U.S. federal holiday, then the next business day will be the actual deadline.

• M.S. in Computer Science
• March 1 : Fall admission
• November 1 : Spring admission

M.S. in Computing, Entrepreneurship and Innovation

M.s. in information systems.

• November 1: Spring Admission

Joint M.S. in Scientific Computing

• February 15: Fall admission, strongly recommended deadline
• May 1: Fall admission, final deadline

Non-Degree Program

• December 1 : Spring admission
• May 1 : Summer admission

PAC Sequence

All ph.d. programs.

• Ph.D.-J.D. applicants must submit two separate applications — one to GSAS, and another to NYU Law. Please consult NYU Law Admissions for the J.D. application deadline.

Requirements

In addition to the general application requirements, the department specifically requires:

Test Scores

Gre optional.

• Ph.D. programs
• Applicants are not expected or required to submit GRE scores. Applicants who wish to submit GRE scores can do so, but need not provide official scores at the time of application.

GRE Not Required

• GRE general test recommended but not required. 

TOEFL/IELTS

Applicants must submit official TOEFL or IELTS scores unless they:

Are a native English speaker; OR

Are a US citizen or permanent resident; OR

Have completed (or will complete) a baccalaureate or master's degree at an institution where the language of instruction is English.

Statement of Academic Purpose

All programs except advanced certificate.

In a concisely written statement, please describe your past and present work as it relates to your intended field of study, your educational objectives, and your career goals. In addition, please include your intellectual and professional reasons for choosing your field of study and why your studies/research can best be done at the Graduate School of Arts and Science at NYU. The statement should not exceed two double-spaced pages.

Advanced Certificate

In a concisely written statement, please answer the following questions:

• Why are you interested in the program?
• What do you want from the program?
• What experience do you have with computer languages? Which ones?
• How skilled are you in these languages?

Writing Sample

Writing sample not required.

Video Statement

Applicants to the Information Systems M.S. program should submit a short video statement. Please read the detailed instructions on the  Videos and Online Materials page .

Special Instructions

Ph.d. program.

The Ph.D. program in Computer Science offers the option to conduct research in New York , or at NYU Abu Dhabi or NYU Shanghai . Applicants to the Abu Dhabi or Shanghai tracks should indicate their interest in the campus section of the application.

Non-degree applicants to Computer Science may only take courses offered through the master’s program. Non-degree applicants who are U.S. citizens or permanent residents, or applicants who hold a current H1-B visa, must use the online application to apply. Other non-degree applicants must follow special instructions. Refer to  Application Policies  for more information. As part of the application, all applicants must provide a final and official academic transcript showing proof that the bachelor’s degree or equivalent was conferred, including all courses with grades received. The TOEFL is optional. The GRE general test is recommended but not required. Letters of recommendation and a résumé are optional. Please leave items blank on the application if you do not provide them. The statement of academic purpose should explain why you want to attend the program as a non-degree student and describe the courses you plan on taking.

Non-degree applicants to the PAC sequence in Computer Science can specify the sequence as a part of the application. All applicants must provide a final and official academic transcript showing proof that the bachelor’s degree or equivalent was conferred, including all courses with grades received. The TOEFL is required for international students whose degree was not instructed in the English language. The GRE general test is not required. Letters of recommendation and a résumé are optional (if you will not be providing either document, please leave these sections of the application blank). The statement of academic purpose should explain why you want to attend the PAC sequence as a non-degree student and describe your goals."

Useful Links

• GSAS Bulletin
• Department Website
• Email (Ph.D.) [email protected]
• Email (CS & IS) [email protected]
• Email (CEI) [email protected]
• Application Portal
• Back to Programs, Requirements and Deadlines

The Graduate School of Arts and Science reserves the right to change this information at any time. This page supersedes all previous versions.

Last updated August 2023.

NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

• Areas & Faculty
• Admission Requirements
• Medical School Track
• NRT FUTURE Program

Advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field: data science. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers who are fluent in the emerging field of data science, and to develop a native environment for their education and training. The CDS PhD Data Science program has rapidly received widespread recognition and is considered among the top and most selective data science doctoral programs in the world. It has recently been recognized by the NSF through an NRT training grant.

The CDS PhD program model rigorously trains data scientists of the future who (1) develop methodology and harness statistical tools to find answers to questions that transcend the boundaries of traditional academic disciplines; (2) clearly communicate to extract crisp questions from big, heterogeneous, uncertain data; (3) effectively translate fundamental research insights into data science practice in the sciences, medicine, industry, and government; and (4) are aware of the ethical implications of their work.

Our programmatic mission is to nurture this new generation of data scientists, by designing and building a data science environment where methodological innovations are developed and translated successfully to domain applications, both scientific and social. Our vision is that combining fundamental research on the principles of data science with translational projects involving domain experts creates a virtuous cycle: Advances in data science methodology transform the process of discovery in the sciences, and enable effective data-driven governance in the public sector. At the same time, the demands of real-world translational projects will catalyze the creation of new data science methodologies. An essential ingredient of such methodologies is that they embed ethics and responsibility by design.

These objectives will be achieved by a combination of an innovative core curriculum, a novel data assistantship mechanism that provides training of skills transfer through rotations and internships, and communication and entrepreneurship modules. Students will be exposed to a wider range of fields than in more standard PhD programs while working with our interdisciplinary faculty. In particular, we are proud to offer a medical track for students eager to explore data science as applied to healthcare or to develop novel theoretical models stemming from medical questions.

In short, the CDS PhD Data Science program prepares students to become leaders in data science research and prepares them for outstanding careers in academia or industry. Successful candidates are guaranteed financial support in the form of tuition and a competitive stipend in the fall and spring semesters for up to five years.* We invite you to learn more through our webpage or by contacting  [email protected] .

*The Ph.D. program also offers students the opportunity to pursue their study and research with Data Science faculty based at NYU Shanghai. With this opportunity, students generally complete their coursework in New York City before moving full-time to Shanghai for their research. For more information, please visit the NYU Shanghai Ph.D. page .

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Computer Science PhD Program

Supervising faculty.

• Program Structure

Current Students

• Application

NYU Shanghai invites applications from exceptional students for PhD study and research in Computer Science. Two programs are available: one offered in partnership with the NYU Graduate School of Arts and Science and the NYU Courant Institute of Mathematical Sciences; and the second offered in partnership with the NYU Tandon School of Engineering and the NYU Department of Computer Science and Engineering.   Participating students are enrolled in either the NYU GSAS Computer Science PhD program or the NYU Tandon Computer Science PhD program, complete their coursework in New York, and then transition to full-time residence at NYU Shanghai where they undertake their doctoral research under the supervision of NYU Shanghai faculty.

Highlights of the Program

• NYU degree upon graduation
• Graduate coursework at NYU New York, either at the Courant Institute or Tandon Department of Computer Science and Engineering
• Research opportunities with and close mentorship by NYU Shanghai faculty
• Access to the vast intellectual resources of the NYU Computer Science community
• Cutting-edge research environment at NYU Shanghai, including the Center for Data Science and Artificial Intelligence, activities such as a regular program of seminars and visiting academics, a thriving community of PhD students, post-doctoral fellows, and research associates, and links with other universities within and outside China
• Financial aid through the NYU Shanghai Doctoral Fellowship , including tuition, fees, and an annual stipend
• Additional benefits exclusive to the NYU Shanghai program, including international health insurance, housing assistance in New York, and travel funds

Theoretical Computer Science, Cryptography, Computational Complexity

Trustworthy Networks, Software Defined Networking, Network Function Virtualization, Cloud & Edge Computing, The Internet of Things

Nasir Memon

Media Forensics, Biometrics, Authentication, Network Security, Data Compression, Cybersecurity​

Machine Learning and Data Mining, Graph Learning, Foundation Model, Multimodal Learning

Shengjie Wang

Machine Learning, Deep Learning, AI for Science, Optimization

Recommender Systems, Data Mining, Human-centered AI

Computational Geometry, Algorithms, Data Structures, Graph Theory, Parameterized Complexity

Natural Language Processing, Human-Computer Interaction, Machine Learning

Recent Publications by NYU Shanghai Faculty

Nick Gravin, Siyao Guo, Tsz Chiu Kwok and Pinyan Lu:  Concentration Bounds for Almost K-wise Independence with Applications to Non-Uniform Security. In SODA 2021.

Siyao Guo, Qian Li, Qipeng Liu and Jiapeng Zhang:  Unifying Presampling via Concentration Bounds. In TCC 2021.

Yevgeniy Dodis, Siyao Guo, Noah Stephens-Davidowitz and Zhiye Xie:  No Time to Hash: Provable Super-Efficient Entropy Accumulation. In CRYPTO 2021.

Yevgeniy Dodis, Siyao Guo, Noah Stephens-Davidowitz and Zhiye Xie:  On Linear Extractors for Independent Sources. In ITC 2021.

Alexander Golovnev, Siyao Guo, Thibaut Horel, Sunoo Park and Vinod Vaikuntanathan: Data Structures Meet Cryptography:  3 SUM with Preprocessing.  In STOC 2020.

Divesh Aggarwal, Siyao Guo, Maciej Obremski, Joao Ribeiro and Noah Stephens-Davidowitz:  Extractor Lower Bounds, Revisited.  In RANDOM 2020.

Kai-Min Chung, Siyao Guo, Qipeng Liu and Luowen Qian:  Tight Quantum Time-Space Tradeoffs for Function Inversion. In FOCS 2020.

Siyao Guo, Pritish Kamath, Alon Rosen, Katerina Sotiraki:  Limits on the Efficiency of (Ring) LWE based Non-Interactive Key Exchange. In PKC 2020 (and Invited to Journal of Cryptology). 

Marshall Ball, Siyao Guo and Daniel Wichs:  Non-Malleable Codes for Decision Trees.  In CRYPTO 2019.

​Liu, Guyue, et al. "Don't Yank My Chain: Auditable {NF} Service Chaining." 18th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 21). 2021.

Ren, Y., Liu, G., Nitu, V., Shao, W., Kennedy, R., Parmer, G., ... & Tchana, A. (2020). Fine-grained isolation for scalable, dynamic, multi-tenant edge clouds. In 2020 {USENIX} Annual Technical Conference ({USENIX}{ATC} 20) (pp. 927-942).

Qiaoyu Tan​

Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan and Zhimeng Jiang. Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs, The Web Conference (WWW), 2023.

Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, and Xia Hu. S2GAE: Self-supervised graph autoencoders are generalizable learners with graph masking. In Proceedings of ACM International Conference on Web Search and Data Mining (WSDM), 2023.

Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi and Xia Hu. Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection, ACM International Conference on Web Search and Data Mining (WSDM), 2023.

Qiaoyu Tan, Xin Zhang, Xiao Huang, Hao Chen, Jundong Li, and Xia Hu. Collaborative graph neural Networks for attributed network embedding. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. 

Sirui Ding, Qiaoyu Tan, Chia-yuan Chang, Na Zou, Kai Zhang, Nathan R. Hoot, Xiaoqian Jiang, and Xia Hu. Multi-task learning for post-transplant cause of death analysis. In Proceedings of AMIA Annual Symposium (AMIA), 2023.

Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li and Ninghao Liu. GiGaMA: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction. ACM International Conference on Information and Knowledge Management (CIKM), 2023.

Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng, Bhargav, Bhushanam, Yuandong Tian, Arun Kejariwal and Xia Hu. DreamShard: Generalizable Embedding Table Placement for Recommender Systems. Neural Information Processing Systems (NeurIPS), 2022.

Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, and Xia Hu. Dynamic memory based attention network for sequential recommendation. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2021.

Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, and Xia Hu. Learning to hash with graph neural networks for recommender systems. In Proceedings of The Web Conference (WWW), 2020.

Machine Learning Force Fields with Data Cost Aware Training. A. Bukharin, T. Liu, S. Wang, S. Zuo, W. Gao, W. Yan, T. Zhao. ICML23

Constrained Robust Submodular Partitioning. S Wang*, T Zhou*, C Lavania, J Bilmes. NeurIPS21

Robust Curriculum Learning: from clean label detection to noisy label self-correction.T Zhou*, S Wang*, J Bilmes. ICLR21

Bias also matters: Bias attribution for deep neural network explanation. S Wang*, T Zhou*, J Bilmes. ICML19

Analysis of deep neural networks with extended data Jacobian matrix. S Wang, A Mohamed, R Caruana, J Bilmes, M Plilipose, M Richardson, K Geras, G Urban, O Aslan. ICML16

Yuanhe Guo, Haoming Liu, and Hongyi Wen. "Towards Personalized Prompt-Model Retrieval for Generative Recommendation." arXiv preprint arXiv:2308.02205 (2023).

Hongyi Wen, Xinyang Yi, Tiansheng Yao, Jiaxi Tang, Lichan Hong, Ed H. Chi. 2022. Distributionally-robust Recommendations for Improving Worst-case User Experience. In Proceedings of the ACM Web Conference 2022 (WWW ’22).

Hongyi Wen, Michael Sobolev, Rachel Vitale, James Kizer, JP Pollak, Frederick Muench, Deborah Estrin. “mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study”. JMIR Mental Health, 2021.

Hongyi Wen, Longqi Yang, Deborah Estrin. “Leveraging post-click feedback for content recommendations”. Proceedings of the 13th ACM Conference on Recommender Systems (RecSys), 2019.

Hongyi Wen, Julian Ramos Rojas, and Anind K. Dey. "Serendipity: Finger gesture recognition using an off-the-shelf smartwatch." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI), 2016.

Haitao Wang*, Jie Xue*, "Near-optimal algorithms for shortest paths in weighted unit-disk graphs". In the 35th International Symposium on Computational Geometry (SoCG), 2019. Also in Discrete & Computational Geometry, 2020.

Pankaj K. Agarwal*, Hsien-Chih Chang*, Subhash Suri*, Allen Xiao*, Jie Xue*, "Dynamic geometric set cover and hitting set". In the 36th International Symposium on Computational Geometry (SoCG), 2020.

Jie Xue, Yuan Li, Rahul Saladi, Ravi Janardan, "Searching for the closest-pair in a query translate". In the 35th International Symposium on Computational Geometry (SoCG), 2019.

Zhao, C., Su, Y., Pauls, A., & Platanios, E. A.  Bridging the generalization gap in text-to-SQL parsing with schema expansion. ACL 2022.

Zhao, C., Xiong, C., Boyd-Graber, J., & Daumé III, H. (2021). Distantly-supervised evidence retrieval enables question answering without evidence annotation. EMNLP 2021.

Zhao, C., Xiong, C., Qian, X., & Boyd-Graber, J. . Complex factoid question answering with a free-text knowledge graph. WWW 2020.

Zhao, C., Xiong, C., Rosset, C., Song, X., Bennett, P., & Tiwary, S. (2020). Transformer-xh: Multi-evidence reasoning with extra hop attention. ICLR 2020.

Selected Faculty and Student Features

" When the Going Gets Tough, the Tough Get Going " (Yanqiu Wu)

" NYU Shanghai Awards First-ever PhD " (Sean Welleck)

" Faculty Spotlight: Guo Siyao " (Siyao Guo)

" Professor Zhang Zheng to Head Amazon's New AI Lab in Shanghai " (Zheng Zhang)

Structure of Program

Participating students complete the PhD degree requirements set by their respective department (either Courant or Tandon CSE) and in accordance with the academic policies of their respective school (either NYU GSAS or NYU Tandon). Each student develops an individualized course plan in consultation with the Director of Graduate Study at the student’s department and the student’s NYU Shanghai faculty advisor. A typical sequence follows:

Begin program with funded research rotation, up to 3 months preceding first Fall semester, to familiarize with NYU Shanghai and faculty as well as lay a foundation for future doctoral study.

Complete PhD coursework in New York alongside other NYU PhD students.

Return to Shanghai for second funded research rotation to solidify relationships with NYU Shanghai faculty and make further progress in research.

Under supervision of NYU Shanghai faculty advisor, pursue dissertation research and continue coursework. Depending on each student’s individualized course of study, return visits to New York may also occur. Complete all required examinations and progress evaluations, both oral and written, leading up to submission and defense of doctoral thesis.

To learn more about the NYU GSAS PhD program degree requirements, please visit this page .

To learn more about the NYU Tandon PhD program degree requirements, please visit this page .

Application Process and Dates

The choice between the NYU GSAS or the NYU Tandon Computer Science program is for each student to decide. Students may apply to either or both.

Applications are to be submitted either through the NYU GSAS Application portal or the NYU Tandon Application portal . Within each portal, students should select the Computer Science PhD as their program of interest, and then indicate their preference for NYU Shanghai by marking the appropriate checkbox when prompted. Applicants will be evaluated by a joint admissions committee of New York and Shanghai faculty. Application requirements are set by each department (either Courant or Tandon CSE ) and are the same as those for all NYU PhD applicants; however, candidates are recommended to elaborate in their application and personal statements about their specific interests in the NYU Shanghai program and faculty.

For admission in Fall 2024, the application deadline is December 1, 2023 with NYU Tandon and December 12, 2023 with NYU GSAS.

Interested students are welcome to contact Vivien Du , program coordinator of the NYU Shanghai Computer Science PhD, via email at [email protected] with any inquiries or to request more information.

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Requirements for Ph.D. in Computer Science

To receive a Ph.D. in Computer Science at the NYU Tandon School of Engineering, a student must:

• satisfy a breadth course requirement, intended to ensure broad knowledge of computer science,
• satisfy a depth requirement, consisting of an oral qualifying exam presentation with a written report, to ensure the student’s ability to do research,
• submit a written thesis proposal and make an oral presentation about the proposal,
• write a Ph.D. thesis that must be approved by a dissertation guidance committee and present an oral thesis defense, and
• satisfy all requirements for the Ph.D. degree, as described in the NYU Tandon School of Engineering bulletin, including credit points, GPA, and time-to-degree requirements.

Upon entering the program, each student will be assigned a faculty advisor who will guide them in formulating an individual study plan directing their course choice for the first two years. The department will hold an annual Ph.D. Student Assessment Meeting, in which all Ph.D. students will be formally reviewed.

1. Credits Requirements and Transfer Credits

In order to obtain a Ph.D. degree, a student must complete a minimum of 75 credits of graduate work beyond the B.S. degree, including at least 21 credits of dissertation. If a student has previously obtained a Master of Science in Computer Science or Computer Engineering, then the credits earned in obtaining that degree may be transferred as blanket credits, subject to rules concerning equivalence of credit hours, up to a maximum of 30 credits. A student who has a Master of Science in another field related to Computer Science may also be eligible for such a blanket transfer of credits, if approved by the departmental Ph.D. Committee (PHDC). If the student has taken computer science courses in a graduate program and is not receiving blanket credits for earning an M.S. degree in that program, those courses are eligible for transfer credits on a course-by-course basis. If the courses are not in computer science, such course-by-course transfer is only allowed with the approval of the Ph.D. Committee (PHDC). The NYU Tandon School of Engineering places some limits on the number and types of transfer credits that are permitted. Applications for transfer credits must be submitted for consideration before the end of the first semester of matriculation. Further details can be found in the section on Graduate Academic Requirements and Policies in the NYU Tandon School of Engineering bulletin.

2. Individual Study Plan

Each incoming Ph.D. student will be assigned to a research advisor, or to an interim advisor, who will provide academic advising until the student has a research advisor. The advisor will meet with the student when the student enters the program to guide the student in formulating an Individual Study Plan. The purpose of the plan is to guide the student’s course choice for the first two years in the program and to ensure that the student meets the breadth requirements. The plan may also specify additional courses to be taken by the student in order to acquire necessary background and expertise. Subsequent changes to the plan must be approved by the advisor.

3. Breadth Requirement

Each Ph.D. student must complete a breadth requirement consisting of 6 courses. To remain in good academic standing, students must fulfill the breadth requirement within 24 months of entering the Ph.D. program.

Students who do not fulfill the breadth requirement within 24 months will be dismissed from the program, unless an exception is granted by the PHDC. The PHDC will consult with the student’s research advisor to decide whether an exception is granted and to determine the conditions the student needs to meet.

Details of Breadth Requirement

The breadth requirement consists of 6 courses: 4 approved list courses, and 2 free choice courses.

The courses used to fulfill the breadth requirement must satisfy the following:

(a) Approved list courses: Four of the courses must be taken from the approved list of courses given in the appendix. The 4 courses must satisfy the following two requirements:

i) Theory requirement: At least one of the 4 courses must be taken in the Theory area.

ii) Systems & Applications Requirement: At least two of the 4 courses must be taken in Systems & Applications.

Exemptions from approved list courses: With the approval of the Ph.D. Committee, students who have previously received a grade of A or A- in a course that is on the approved list, while enrolled in another NYU graduate program, can use that course towards the breadth requirement in lieu of taking it while in the Ph.D. program. Also, students who have previously received a grade of A or A- in a graduate course similar to one on the approved list, while enrolled in a graduate program at NYU or at another university with standards comparable to those at NYU, can use that course in lieu of taking the course on the approved list. The determination of whether a previously taken course, not on the approved list, can be used in this way, will be made by the PHDC. However, any student who uses courses taken prior to entering the CS Ph.D. program to fulfill one or both of the Systems & Applications course requirements must work on a medium-sized or larger software project while in the CS Ph.D. program. This project can be part of coursework or the student’s research. A brief report on the project must be produced and approved by the PHDC.

Approved Course List: The list of approved courses will be reviewed regularly by the PHDC and is subject to change. Any changes must be approved by the CSE Department. In order for a course to be considered for inclusion in the list, the course must be rigorous and the students in it must be evaluated individually. Examples of inappropriate courses include those in which students are traditionally not differentially evaluated (e.g., all students receive A’s or “pass”) and courses in which grades are based on attendance or making a presentation of someone else’s work, rather than on tests and assignments.

Students, under their advisors’ guidance, should select their courses from the approved list so that they are exposed to a broad set of topics in computer science.

(b) Free choice courses: Students must take 2 free choice courses in addition to the 4 required courses from the approved list. Students can use any graduate course at NYU as free choice courses, but must obtain advisor approval to use a course not on the approved list. Students cannot use independent study courses (such as Advanced Project CS-GY 9963 or Readings in Computer Science, CS-GY 9413 and CS-GY 9423) or dissertation. Both free choice courses must be taken while in the CS Ph.D. program. No exemptions are available for free choice courses. 

(c) GPA requirement: Students must receive a grade of at least B in each of the six courses used to fulfill the breadth requirement. The average in the 4 approved list courses used to fulfill the breadth requirement must be at least 3.5. Students who take more than 4 approved list courses can choose which ones to apply towards the breadth requirement, and the average will be calculated using just those courses. (For students who receive exemptions allowing them to take fewer than 4 approved list courses while in the CS Ph.D. program, the average will be calculated over the approved list courses that were taken while in the CS Ph.D. program . Students may choose not to use all exemptions which have been granted to them, and may instead take additional approved list courses to be used to satisfy the breadth requirement.)  The average in the 2 free choice courses must also be at least 3.5.

(d) Requirement for Students who have never taken an Algorithms Course:  Any student who has not taken a course in Algorithms prior to entering the Ph.D. program, at either the undergraduate or the graduate level, must take a graduate course in algorithms while in the Ph.D. program.  Students may take CS-GY 6033 (Design and Analysis of Algorithms I), CS-GY 6043 (Design and Analysis of Algorithms II), or CSCI-GA.3520 (Honors Analysis of Algorithms) to fulfill this requirement. The department may revise this list in the future depending on course offerings. Alternatively, students may petition the PHDC to use another course. The grade received in the course must be at least B.

4. Depth Requirement

By the end of a student’s third semester in the program, at the latest, the student must be involved in a research project under the guidance of a faculty research advisor. It is the responsibility of each student to find a faculty advisor and a research project, and to inform the PHDC Chair about their choice of advisor. Students must inform the PHDC chair if they change their research advisor.

To satisfy the depth requirement, students must take a qualifying exam (QE) based on their research. The QE must be passed before the start of the student’s fifth semester in the program. Students are required to form a QE committee, select an exam topic, and a tentative date approved by the advisor and committee, by the end of their third semester.

Scheduling the QE less than two months before the start of the fifth semester is strongly discouraged. If a student does not pass the QE before the fifth semester, the student will not be allowed to continue the Ph.D. in the fifth semester, unless an exception is granted by the PHDC and the Office of Graduate Academics.

Students must register for RE-GY 9990 CS01, a 0-credit course, at the start of the semester in which they will take the QE.

The QE committee must consist of the advisor and at least two other members. The committee must be approved by the advisor and the PHDC. The advisor is the designated chair of the committee. All members of the QE committee must be CSE faculty, faculty from other departments at NYU, or individuals of like standing from outside the university. At least two of the QE committee members must be tenured or tenure-track members of the CSE department, unless permission is obtained from the PHDC to include only one such member.

For the QE, the student must give an oral presentation of their research accomplishments to the QE committee and write a detailed document describing those accomplishments. The document must be submitted to the QE committee and the PHDC no later than one week before the oral presentation. A student is expected to have conducted original research by the time of the exam. This research may have been carried out independently or in collaboration with faculty, research staff, or other students. Students are encouraged, but not required, to have publication-worthy results by the time of the exam. It is not sufficient for a student to present a survey of previous work in an area or a reimplementation of algorithms, techniques, or systems developed by others.

The committee, by majority vote, gives a grade for the QE as either “Pass” or “Fail”. The chair of the QE committee will send this grade in writing to the student and to the PHDC chair, together with a written evaluation of the student’s performance, approved by the QE committee members. A student who does not receive a “Pass” may request permission from the PHDC to retake the exam. The PHDC will consult with the QE committee, review the case and make the final decision as to whether a retake is allowed or not. A student may petition the PHDC to change one or more members of the QE committee, but approval of the request will be at the PHDC’s discretion.

If the request for a retake is approved, the QE committee will determine the date for the second attempt. If the student is not allowed to retake the exam, the student will not be allowed to continue in the Ph.D. program in the following semester. If the student does not pass the qualifying exam on the second attempt, or otherwise does not satisfy the conditions given to them upon failing the exam the first time, the student will not be allowed to continue in the Ph.D. program in the following semester.

If a student has passed the QE and then changes their area of research, the student need not retake the QE.

5. Thesis Proposal and Presentation

Within 6 months of passing the QE, each student is required to form a dissertation guidance committee. This committee must be approved by the student’s research advisor and by the PHDC. The committee must include at least four members, including the research advisor. The committee members can be CSE faculty, faculty from other departments at NYU, or individuals of like standing from outside the university. At least one member of the dissertation guidance committee must be a tenured or tenure-track CSE faculty member, and at least one member of the committee must be from outside the CSE department. The committee chairperson may or may not be the research advisor, but must be a tenured or tenure-track faculty member in Tandon or have a cross-appointment of at least Associate level.

By the end of the student’s fifth semester in the program, the student and committee must set a tentative date for the thesis proposal presentation. The presentation must be done prior to the start of the student’s seventh semester in the program.

Before finalizing the date of the presentation, the student must submit a written thesis proposal to the dissertation guidance committee which should include:

• a description of the research topic
• an explanation of how the research will advance the state of the art, and
• a tentative research plan

After the dissertation guidance committee has approved the thesis proposal, the student should schedule the thesis proposal presentation and notify the PHDC chair once this has been finalized. The presentation should be announced to the faculty by the PHDC chair at least one week before it occurs. The presentation is open to all faculty. It may also be open to others at the discretion of the research advisor.

Substantial subsequent changes to the thesis topic must be approved by the dissertation guidance committee.

6. Thesis and Thesis Defense

The last, and most substantial, aspect of the Ph.D. program is the dissertation. The research for the dissertation should be conducted in close consultation with the research advisor. When the advisor determines that the student is ready to defend the thesis, a dissertation defense will be scheduled. For the defense, the student will give an oral presentation describing the thesis research, which is open to the public. Following the oral presentation and an initial question and answer session, the dissertation committee and CSE faculty may ask the student further questions in closed session.

Other requirements for the Ph.D. dissertation and defense can be obtained from the Office of the Associate Dean for Graduate Academics in the NYU Tandon School of Engineering.

7. Annual Ph.D. Student Assessment Meeting

All Ph.D. students will be formally reviewed each year in a Ph.D. Student Assessment Meeting. The review is conducted by the entire CSE faculty and includes at least the following items (in no particular order):

• All courses taken, grades received, and GPAs.
• Research productivity: publications, talks, software, systems, etc.
• Faculty input, especially from advisors and committee members.
• Student’s own input.
• Cumulative history of the student’s progress.

As a result of the review, each student will be placed in one of the following two categories, by vote of the faculty:

• In Good Standing: The student has performed well in the previous semester and may continue in the Ph.D. program for one more year, assuming satisfactory academic progress is maintained.
• Not in Good Standing: The student has not performed sufficiently well in the previous year. The consequences of not being in good standing will vary, and may include being placed on probation, losing RA/GA/TA funding, or not being allowed to continue in the Ph.D. program.

Following the review, students will receive formal letters which will inform them of their standing. The letters may also make specific recommendations to the student as to what will be expected of them in the following year. A copy of each student’s letter will be placed in the student’s file.

8. NYU Tandon School of Engineering Requirements

Other requirements can be found in the NYU Tandon School of Engineering Bulletin. Students must meet all applicable requirements, including credit points, GPA, and time-to-degree requirements.

The following courses at NYU Tandon School of Engineering can be used to satisfy the breadth requirements:

• CS-GY 6043 Design and Analysis of Algorithms II 3 Credits
• CS-GY 6703 Computational Geometry 3 Credits
• CS-GY 6753 Theory of Computation 3 Credits
• CS-GY 6763 Algorithmic Machine Learning and Data Science 3 Credits

Systems & Applications

• CS-GY 6083 Principles of Database Systems 3 Credits
• CS-GY 6093 Advanced Database Systems 3 Credits
• CS-GY 6143 Computer Architecture II 3 Credits
• CS-GY 6243 Operating Systems II 3 Credits
• CS-GY 6253 Distributed Operating Systems 3 Credits
• CS-GY 6313 Information Visualization 3 Credits
• CS-GY 6413 Compiler Design and Construction 3 Credits
• CS-GY 6513 Big Data 3 Credits
• CS-GY 6533 Interactive Computer Graphics 3 Credits
• CS-GY 6543 Human Computer Interaction 3 Credits
• CS-GY 6553 Game Design 3 Credits
• CS-GY 6613 Artificial Intelligence I 3 Credits
• CS-GY 6823 Network Security 3 Credits
• CS-GY 6843 Computer Networking 3 Credits
• CS-GY 6913 Web Search Engines 3 Credits
• CS-GY 6923 Machine Learning 3 Credits
• CS-GY 6943 Artificial Intelligence for Games 3 Credits
• CS-GY 9163 Application Security 3 Credits

The following courses, offered by the Computer Science Department at the Courant Institute of Mathematical Sciences at NYU , can also be used to satisfy the breadth requirements:

• CSCI-GA 3520 Honors Analysis of Algorithms
• CSCI-GA.2243 High Performance Computer Architecture
• CSCI-GA.2270 Computer Graphics
• CSCI-GA.2271 Computer Vision
• CSCI-GA.2434 Advanced Database Systems
• CSCI-GA.2560 Artificial Intelligence
• CSCI-GA.2565 Machine Learning
• CSCI-GA.2566 Foundations of Machine Learning
• CSCI-GA.2590 Natural Language Processing
• CSCI-GA.2620 Networks and Distributed Systems
• CSCI-GA.3110 Honors Programming Languages
• CSCI-GA.3130 Honors Compilers
• CSCI-GA.3250 Honors Operating Systems

Students who began the program before Fall 2015 have the option of completing the requirements that were in effect at the time they began the program.

Students who began the program before Fall 2017 may count CS-GY 6903 Applied Cryptography    as a breadth course in the Theory category, and CS-GY 6063 Software Engineering I    as a breadth course in the Systems and Applications category.

Prospective Master's Students

New MS program: Computing, Entrepreneurship, and Innovation (MS-CEI)

The Department of Computer Science offers three M.S. degree programs, Masters in Computer Science (MSCS), Masters in Information Systems (MSIS), and Master's in Computing, Entrepreneurship and Innovation (MS-CEI) for all prospective students. If you are an existing NYU CS undergraduate, we also offer an accelerated BA-MS program.

Students who obtain a Master's of Science in Computer Science are qualified to do significant development work in the computer industry or important application areas. Those who receive a doctoral degree are in a position to hold faculty appointments and do research and development work at the forefront of this rapidly changing and expanding field. Additionally, the department offers a Masters of Science in Information Systems in collaboration with the Stern School of Business. The emphasis in the MS in Information Systems program is on the use of computer systems in business. The Master of Science in Scientific Computing, just established by the Mathematics and Computer Science Departments, is designed to provide broad training in areas related to scientific computing using modern computing technology and mathematical modeling arising in various applications.

Established in 1969 as part of the Courant Institute of Mathematical Sciences, the department has experienced substantial growth in its faculty, student body, research staff, and funding. Research areas include algorithmics, computational geometry, high-level programming languages, compilers and compiler optimization techniques, parallel and distributed computing, design of computer systems, databases, artificial intelligence, natural language processing, graphics, multimedia, computer vision, mathematical programming, numerical analysis, computational biology and computational finance.

The core of the curriculum consists of courses in algorithms, programming languages, compilers, artificial intelligence, database systems, and operating systems. Advanced courses are offered in many areas such as natural language processing, the theory of computation, computer vision, software engineering, compiler optimization techniques, computer graphics, distributed computing, multimedia, networks, cryptography and security, and computational finance. Adjunct faculty, drawn from outside academia, teach special topics courses in their areas of expertise.

MSCS (Master's in Computer Science) ▶

Msis (master's in information systems) ▶, ms-cei (master's in computing, entrepreneurship, and innovation) ▶, preparatory courses (pac) ▶, ms program admissions ▶.

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Global PhD Student Fellowship in Computer Science

The NYU Abu Dhabi Global PhD Student Fellowship is offered through two Computer Science doctoral programs at NYU New York.

• Courant Institute at NYU Graduate School of Arts and Science
• Computer Science and Engineering Department at NYU Tandon School of Engineering

The programs generally involve one year of classwork at NYU New York, followed by three to four years of research at NYU Abu Dhabi, depending on the NYU New York program. If selected, the doctorate is fully funded under the NYU Abu Dhabi Global PhD Student Fellowship.

Key Features of the Fellowship

• New York University degree upon graduation
• Access to the extraordinary resources of the Courant Institute and the Tandon School of Engineering
• Graduate coursework in New York
• Cutting-edge research opportunities in NYU Abu Dhabi’s labs
• Tuition, fees, and health insurance provided throughout the program
• Generous research assistantship and stipend provided by NYU Abu Dhabi throughout the program
• Assistance for degree-related travel between Abu Dhabi and New York
• Campus accommodation at no cost in Abu Dhabi
• A contribution toward accommodation costs in New York
• Career development assistance at both campuses

For more information about our programs, please contact  [email protected] .

Interested in Applying?

Get in touch with us.

Learn How to Apply

The applications for Fall 2024 are now closed. Applications will re-open for Fall 2025 in September.

“He’s the Best Person to Work With”

Global PhD Fellow Nasser Zalmout is working with a highly regarded mentor to improve how computers translate Arabic.