Computer Science PhD Program

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
 

Supervising Faculty

  • Siyao Guo

    Siyao Guo

    Theoretical Computer Science, Cryptography, Computational Complexity

  • Guyue Liu

    Guyue Liu

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

  • Keith Ross

    Keith Ross

    Deep Learning, Reinforcement Learning

  • Gus xia

    Gus Xia

    Computer Music, Artificial Music Intelligence, Human-Computer Interaction, Bio-Music Computing

  • Jie Xue

    Jie Xue

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

Recent Publications by NYU Shanghai Faculty

 
  • 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.
  • 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.
  • 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.
  • Yiming Zhang and Keith Ross, On-Policy Deep Reinforcement Learning for the Average-Reward Criterion, ICML 2021
  • Xinyue Chen, Che Wang, Zijian Zhou, Keith Ross, Randomized Ensembled Double Q-Learning: Learning Fast without a Model, International Conference on Learning Representations  (ICLR) 2021
  • Kai-Min Chung, Siyao Guo, Qipeng Liu and Luowen Qian:  Tight Quantum Time-Space Tradeoffs for Function Inversion. In FOCS 2020.
  • J. Jiang and G. Xia.Transformer VAE: A Hierarchical Model for Structure-aware and Interpretable Music Representation Learning,   Proc. 45th International Conference on Acoustics, Speech and Signal Processing, 2020
  • Divesh Aggarwal, Siyao Guo, Maciej Obremski, Joao Ribeiro and Noah Stephens-Davidowitz:  Extractor Lower Bounds, Revisited.  In RANDOM 2020.
  • Alexander Golovnev, Siyao Guo, Thibaut Horel, Sunoo Park and Vinod Vaikuntanathan: Data Structures Meet Cryptography:  3 SUM with Preprocessing.  In STOC 2020.
  • Yiming Zhang, Quon Voung, Keith Ross, First Order Constrained Optimization in Policy Space, NeurIPS (spotlight paper), 2020
  • Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith Ross, Best Action Imitation Learning for Batch Reinforcement Learning, NeurIPS 2020
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • R. Yang, D. Wang, Z. Wang, T. Chen, J. Jiang and G. Xia. Deep Music Analogy Via Latent Representation Disentanglement, Proc. 20th the International Conference on Music Information Retrieval (ISMIR), 2019
  • J. Jiang, G. Xia, and R. Dannenberg. Representing Music Structure by Variational Attention,ML4MD workshop at ICML, 2019

Selected Faculty and Student Features

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

"Faculty Spotlight: Guo Siyao" (Siyao Guo)

"Keith Ross on Data Driven Privacy Research" (Keith Ross)

"Music and Machine: A Musician's Quest to Humanize AI" (Gus Xia)

"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:

Summer 1
in Shanghai

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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.

Year 1
(Fall and Spring)
in New York

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

Summer 2
in Shanghai

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

Year 2
through Year 5
in Shanghai

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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.

 

Current Students

Name Research Areas
Tianyao Chen Artificial Music Intelligence
Structure Analysis of Sequences
Junyan Jiang Machine Learning, Computer Music, Audio Signal Processing, Representation Learning
Nanfeng Qin Computer Music
Che Wang Reinforcement Learning
Ziyu Wang Computer Music, Representation Learning
Sean Welleck Structured Prediction, Deep Learning
Yanqiu Wu Data Mining, Deep Reinforcement Learning
Zhiye Xie Theoretical Computer Science
Yiming Zhang Deep Learning, Reinforcement Learning, Generative Models

Alumni

Name Placement
Sean Welleck, Ph.D. ‘21 Postdoctoral Scholar, University of Washington
 

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.

The NYU GSAS and NYU Tandon Application portal is now open for Fall 2022 admission, and the deadlines are December 12, 2021 for NYU GSAS & December 1, 2021 for NYU Tandon. Applications are only accepted for Fall admission.

 

Contact Us

Interested students are welcome to contact Vivien Du, program coordinator of the NYU Shanghai Computer Science PhD, via email at shanghai.phd@nyu.edu with any inquiries or to request more information.