Rong Ge, Cue Family Associate Professor of Computer Science

Theoretical computer science and machine learning.

Contact Info:

Office Location:
Email Address:
Web Page:	https://users.cs.duke.edu/~rongge/

Teaching (Fall 2025):

• COMPSCI 701S.01, INTRO GRAD STUDENTS COMPSCI Synopsis

  LSRC B101, F 10:05 AM-11:20 AM

Education:

Ph.D.

Princeton University

2013

Recent Publications   (More Publications)

1. Chidambaram, M; Ge, R, ON THE LIMITATIONS OF TEMPERATURE SCALING FOR DISTRIBUTIONS WITH OVERLAPS, 12th International Conference on Learning Representations Iclr 2024 (January, 2024)  [abs]
2. Xie, R; Wang, J; Huang, R; Zhang, M; Ge, R; Pei, J; Gong, NZ; Dhingra, B, RECALL: Membership Inference via Relative Conditional Log-Likelihoods, Emnlp 2024 2024 Conference on Empirical Methods in Natural Language Processing Proceedings of the Conference (January, 2024), pp. 8671-8689 [doi]  [abs]
3. Chen, Z; Ge, R, Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input, Advances in Neural Information Processing Systems, vol. 37 (January, 2024)  [abs]
4. Vladymyrov, M; von Oswald, J; Sandler, M; Ge, R, Linear Transformers are Versatile In-Context Learners, Advances in Neural Information Processing Systems, vol. 37 (January, 2024)  [abs]
5. Zhou, M; Ge, R, How does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks, Advances in Neural Information Processing Systems, vol. 37 (January, 2024)  [abs]

Recent Grant Support

• Collaborative Reseach: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable NETworks (THEORINET), National Science Foundation, 2020/09-2026/08.      
• Collaborative Reseach: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable NETworks (THEORINET), Simons Foundation, 2020/09-2025/08.      
• CAREER: Optimization Landscape for Non-convex Functions - Towards Provable Algorithms for Neural Networks, National Science Foundation, 2019/07-2024/06.      
• HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms, National Science Foundation, 2019/10-2023/09.      
• Ge Sloan Fellowship 2019, Alfred P. Sloan Foundation, 2019/09-2023/09.