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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JW

Research

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.

News

Prior to 2025
  • [09/2024] Three papers accepted to NeurIPS 2024; huge congrats to Yuhang, Licong, and Ruiqi!!
  • [05/2024] One paper accepted to COLT 2024.
  • [03/2024] Invited talk at UCLA CS.
  • [02/2024] Invited talk at UC Berkeley Biostatistics.
  • [01/2024] Two papers accepted to ICLR 2024.
  • [10/2023] Selected as Rising Star in Data Science by UChicago and UCSD.
  • [09/2023] Two papers accepted to NeurIPS 2023.
  • [08/2023] Joining the Simons Institute at UC Berkeley as a postdoc.
  • [06/2023] Defended my PhD dissertation!
  • [05/2023] One paper accepted to CoLLAs 2023; congrats to Haoran!
  • [04/2023] One paper accepted to ICML 2023.
  • [09/2022] Two papers accepted to NeurIPS 2022.
  • [06/2022] Interning at Google Research Seattle.
  • [05/2022] One paper accepted to ICML 2022 as long presentation!
  • [01/2022] One paper accepted to AISTATS 2022.
  • [12/2021] Passed the PhD candidacy exam.
  • [09/2021] Two papers accepted to NeurIPS 2021.
  • [05/2021] Awarded MINDS Summer Data Science Fellowship!
  • [05/2021] One paper accepted to COLT 2021.
  • [02/2021] In a relationship with Yuan, happy Valentine's day~
  • [01/2021] One paper is accepted to ICLR 2021.
  • [05/2020] Two papers accepted to ICML 2020.
  • [09/2019] Joining Hopkins. Veritas vos liberabit!
  • [06/2019] Graduated from Peking University.
  • [04/2019] One paper accepted to ICML 2019.
  • [03/2019] One paper accepted to CVPR 2019 as an oral presentation.
  • [12/2018] Looking for a Ph.D. position in machine learning.

Selected Papers (All Papers)

* indicates equal contribution, † indicates alphabetical order.

Optimization with Large Stepsizes

Statistical Approaches to Implicit Regularization

Applications of Implicit Regularization

Margin Theory for Neural Networks

Invited Talks

Services

People

Family

Recent Student Collaborators

Yuhang Cai    Licong Lin    Ruiqi Zhang   

Collaborators

Amir Abedsoltan    Peter Bartlett    Mikhail Belkin    Vladimir Braverman    Zixiang Chen    Yihe Deng    Udaya Ghai    Quanquan Gu    Peter Kairouz    Sham Kakade    Jason Lee    Haoran Li    Xuheng Li    Michael Lindsey    Song Mei    Depen Morwani    Adit Radhakrishnan    Matus Telgarsky    Nikhil Vyas    Lin Yang    Bin Yu    Hanlin Zhang    Wennan Zhu    Dongruo Zhou    Kangjie Zhou    Difan Zou