Jiawei Zhang

Postdoctoral scholar in LIDS @ MIT

Massachusetts Institute of Technology

Biography

Jiawei Zhang is currently a postdoctoral scholar in the Laboratory for Information & Decision Systems (LIDS) at MIT, working with Prof. Asuman Ozdaglar. He also works closely with Prof. Saurabh Amin. He obtained the Ph.D. degree in Computer and Information Engineering from the Chinese University of Hong Kong (CUHK), with an advisory of Prof. Zhi-Quan (Tom) Luo. Previously, he obtained the B.Sc. in Mathematics (Hua Loo-Keng Talent Program) from the University of Science and Technology of China.

His research interests include optimization theory, designing efficient optimization algorithms, and their applications to machine learning and data science. Recently, he is very interested in understanding machine learning especially deep learning from an optimization perspective.

He organized two sessions at INFORMS Annual Meeting 2022 on nonconvex nonsmooth optimization and nonconvex constrained optimization.

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Interests

Nonlinear and convex optimization: theory and algorithms
Learning algorithms: robustness and generalization
Data-driven decision-making under uncertainty
New computational models for AI-driven platforms, sustainable energy systems, and signal processing

Education

Ph.D. in Computer and Information Engineering

The Chinese University of Hong Kong (CUHK)
B.Sc. in Mathematics (Hua Loo-Keng Talent Program)

University of Science and Technology of China (USTC)

Recent Publications

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Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang (2023). Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation. Proceedings of the 40th International Conference on Machine Learning (ICML 2023).

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Prashant Khanduri, Ioannis Tsaknakis, Yihua Zhang, Jia Liu, Sijia Liu, Jiawei Zhang, Mingyi Hong (2023). Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach. Proceedings of the 40th International Conference on Machine Learning (ICML 2023).

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Jiawei Zhang, Wenqiang Pu, Zhi-Quan Luo (2022). On the Iteration Complexity of Smoothed Proximal ALM for Nonconvex Optimization Problem with Convex Constraints. arXiv preprint arXiv:2207.06304.

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Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang (2022). What is a Good Metric to Study Generalization of Minimax Learners?. Advances in Neural Information Processing Systems 35 (NeurIPS 2022).

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Jiawei Zhang, Zhi-Quan Luo (2022). A Global Dual Error Bound and Its Application to the Analysis of Linearly Constrained Nonconvex Optimization. SIAM Journal on Optimization.

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