Jingfeng Wu  |  吴京风

I am a postdoc fellow (2023 - now) at the Simons Institute at UC Berkeley hosted by Peter Bartlett and Bin Yu, as a part of the NSF/Simons Collaboration on the Theoretical Foundations of Deep Learning.

I earned my Ph.D. in Computer Science (2019 - 2023) at Johns Hopkins University, advised by Vladimir Braverman. Before that, I obtained B.S. in Mathematics (2012 - 2016) and M.S. in Applied Math (2016 - 2019) from Peking University.

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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.

Specifically, I am interested in the theory and algorithms of deep learning, as well as related topics in algorithms, machine learning, optimization, and statistical learning theory.

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Conference Papers


Conference Reviewer

  • International Conference on Machine Learning (ICML), 2020 - 2023
  • Conference on Neural Information Processing Systems (NeurIPS), 2020 - 2023
  • International Conference on Learning Representations (ICLR), 2021 - 2024
  • International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 - 2023
  • Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Conference Program Committee Member

  • AAAI Conference on Artificial Intelligence (AAAI), 2021 - 2023

Journal Reviewer

  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • Journal of Machine Learning Research (JMLR)
  • Transactions on Machine Learning Research (TMLR)
  • Applied Probability Journals