Jingfeng Wu  |  吴京风

I am a postdoc fellow at the Simons Institute at UC Berkeley hosted by Peter Bartlett and Bin Yu. I am a part of the NSF/Simons Collaboration on the Theoretical Foundations of Deep Learning.

I obtained my Ph.D. in Computer Science at Johns Hopkins University, advised by Vladimir Braverman, and B.S. and M.S. at Peking University. I was selected as a 2023 Rising Star in Data Science by UChicago and UCSD.

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My research focuses on bridging the gap between theory and practice in machine learning by developing efficient algorithms to solve real-world problems and providing a deep understanding of the underlying theoretical principles.

I work on the theory and algorithms of deep learning, and related topics in algorithms, machine learning, optimization, and statistical learning theory.



* indicates equal contribution, † indicates alphabetical order.


Refereed Papers

Invited Talks




Recent Student Collaborators

Yuhang Cai    Licong Lin    Ruiqi Zhang   


Peter L. Bartlett    Vladimir Braverman    Zixiang Chen    Yihe Deng    Quanquan Gu    Peter Kairouz    Sham M. Kakade    Jason D. Lee    Haoran Li    Xuheng Li    Michael Lindsey    Song Mei    Matus Telgarsky    Lin F. Yang    Bin Yu    Wennan Zhu    Dongruo Zhou    Difan Zou