Jingfeng Wu  |  吴京风

Jingfeng Wu is a postdoctoral fellow at the Simons Institute for the Theory of Computing at UC Berkeley. His research focuses on deep learning theory, optimization, and statistical learning. He earned his Ph.D. in Computer Science from Johns Hopkins University in 2023. Prior to that, he received a B.S. in Mathematics (2016) and an M.S. in Applied Mathematics (2019), both from Peking University. In 2023, he was recognized as a Rising Star in Data Science by the University of Chicago and UC San Diego.

• [12/2025] Invited talk at TTIC.
• [12/2025] Presenting a tutorial at NeurIPS 2025 with Yu-Xiang and Maryam.
• [09/2025] Invited talks at Yale, UPenn, JHU, MIT, Harvard, NYU, Columbia.
• [09/2025] Two papers accepted to NeurIPS 2025.
• [07/2025] Invited talk at the 6th Youth in High-Dimensions Conference.
• [05/2025] Invited talk at SIAM DS25.
• [05/2025] Three papers accepted to ICML 2025.
• [02/2025] Organizing a deep learning theory workshop at Simons.
• [01/2025] One paper accepted to ICLR 2025.
• [09/2024] Three papers accepted to NeurIPS 2024.
• [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.

• A Statistical View on Implicit Regularization: GD Dominates Ridge
  • [12/2025] TTIC, Talk at TTIC, hosted by Nati Srebro
  • [10/2025] Columbia, Machine Learning and AI Seminar, hosted by Daniel Hsu
  • [10/2025] NYU, Math and Data Seminar, hosted by Matus Telgarsky
  • [09/2025] Yale, Statistics & Data Science Seminar, hosted by Theodor Misiakiewicz, Omar Montasser
• A Statistical View on Implicit Regularization: GD for Logistic Regression
  • [07/2025] ICTP, 6th Youth in High-Dimensions Conference, hosted by Marco Mondelli et al.
  • [02/2025] UC Berkeley, Deep Learning Theory Workshop, hosted by Peter Bartlett et al.
• Reimagining Gradient Descent: Large Stepsize, Oscillation, and Acceleration
  • [10/2025] Harvard, Talk at Kempner, hosted by Sham Kakade
  • [09/2025] MIT, Talk at LIDS, hosted by Pablo Parrilo
  • [09/2025] JHU, CS Theory Seminar, hosted by Vova Braverman
  • [09/2025] UPenn, Group Seminar, hosted by Jason Altschuler
  • [06/2025] MPI & UCLA, Math Machine Learning Seminar, hosted by Guido Montufar
  • [05/2025] SIAM DS25, Dynamical Systems for Machine Learning, hosted by Molei Tao
  • [01/2025] UCLA, Level Set Meeting, hosted by Shu Liu, Stanley Osher
  • [09/2024] Simons Foundation, MoDL Annual Meeting, hosted by Peter Bartlett, Rene Vidal
  • [05/2024] UC San Diego, MoDL Collaboration Meeting, hosted by Chaoyue Liu et al.
  • [03/2024] UCLA, Computer Science Seminar, hosted by Quanquan Gu
  • [02/2024] UC Berkeley, Biostatistics Seminar, hosted by Lexin Li
  • [02/2024] UC San Diego, Group Seminar, hosted by Mikhail Belkin