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
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Siyao Guo
Theoretical Computer Science, Cryptography, Computational Complexity
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Guyue Liu
Trustworthy Networks, Software Defined Networking, Network Function Virtualization, Cloud & Edge Computing, The Internet of Things
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Nasir Memon
Media Forensics, Biometrics, Authentication, Network Security, Data Compression, Cybersecurity
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Qiaoyu Tan
Machine Learning and Data Mining, Graph Learning, Foundation Model, Multimodal Learning
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Shengjie Wang
Machine Learning, Deep Learning, AI for Science, Optimization
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Hongyi Wen
Recommender Systems, Data Mining, Human-centered AI
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Jie Xue
Computational Geometry, Algorithms, Data Structures, Graph Theory, Parameterized Complexity
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Chen Zhao
Natural Language Processing, Human-Computer Interaction, Machine Learning
Recent Publications by NYU Shanghai Faculty
- 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.
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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.
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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.
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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.
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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.
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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.
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Machine Learning Force Fields with Data Cost Aware Training. A. Bukharin, T. Liu, S. Wang, S. Zuo, W. Gao, W. Yan, T. Zhao. ICML23
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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.
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Zhao, C., Su, Y., Pauls, A., & Platanios, E. A. Bridging the generalization gap in text-to-SQL parsing with schema expansion. ACL 2022.
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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.
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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.
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Constrained Robust Submodular Partitioning. S Wang*, T Zhou*, C Lavania, J Bilmes. NeurIPS21
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Robust Curriculum Learning: from clean label detection to noisy label self-correction.T Zhou*, S Wang*, J Bilmes. ICLR21
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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.
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Siyao Guo, Qian Li, Qipeng Liu and Jiapeng Zhang: Unifying Presampling via Concentration Bounds. In TCC 2021.
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Yevgeniy Dodis, Siyao Guo, Noah Stephens-Davidowitz and Zhiye Xie: No Time to Hash: Provable Super-Efficient Entropy Accumulation. In CRYPTO 2021.
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Yevgeniy Dodis, Siyao Guo, Noah Stephens-Davidowitz and Zhiye Xie: On Linear Extractors for Independent Sources. In ITC 2021.
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Zhao, C., Xiong, C., Boyd-Graber, J., & Daumé III, H. (2021). Distantly-supervised evidence retrieval enables question answering without evidence annotation. EMNLP 2021.
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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).
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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.
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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.
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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.
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Alexander Golovnev, Siyao Guo, Thibaut Horel, Sunoo Park and Vinod Vaikuntanathan: Data Structures Meet Cryptography: 3 SUM with Preprocessing. In STOC 2020.
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Divesh Aggarwal, Siyao Guo, Maciej Obremski, Joao Ribeiro and Noah Stephens-Davidowitz: Extractor Lower Bounds, Revisited. In RANDOM 2020.
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Kai-Min Chung, Siyao Guo, Qipeng Liu and Luowen Qian: Tight Quantum Time-Space Tradeoffs for Function Inversion. In FOCS 2020.
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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).
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Zhao, C., Xiong, C., Qian, X., & Boyd-Graber, J. . Complex factoid question answering with a free-text knowledge graph. WWW 2020.
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Zhao, C., Xiong, C., Rosset, C., Song, X., Bennett, P., & Tiwary, S. (2020). Transformer-xh: Multi-evidence reasoning with extra hop attention. ICLR 2020.
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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.
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Bias also matters: Bias attribution for deep neural network explanation. S Wang*, T Zhou*, J Bilmes. ICML19
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Marshall Ball, Siyao Guo and Daniel Wichs: Non-Malleable Codes for Decision Trees. In CRYPTO 2019.
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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
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:
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.
(Fall and Spring)
in New York
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Complete PhD coursework in New York alongside other NYU PhD students.
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.
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 |
| Zixuan Dong | Reinforcement Learning, Machine Learning |
| Junyan Jiang | Machine Learning, Computer Music, Audio Signal Processing, Representation Learning |
| Jiajin Liu | Computer Networks |
| Liwei Lin | Computer music, Representation Learning, Audio Signal Processing |
| Runwei Lu | Network Simulations, Security and Privacy, Deep Learning |
| Nanfeng Qin | Computer Music |
| Yuejie Wang | Computer Networks |
| Ziyu Wang | Computer Music, Representation Learning |
| Zhiye Xie | Theoretical Computer Science |
| Haoming Liu | Multi-modal AI, Personalization Systems |
Alumni
| Name | Placement |
| Sean Welleck | Postdoctoral Scholar, University of Washington |
| Yiming Zhang | Research Scientist at Lyft |
| Yanqiu Wu | Postdoc at CSIRO in Australia |
| Che Wang | Postdoctoral Scientist at Amazon |
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 application deadlines have passed and the NYU GSAS and NYU Tandon Application portals are closed. If you are interested in applying in the future, applications will reopen in Fall 2023 for 2024 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.
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