Quanquan Gu

I am an Assistant Professor of Computer Science at UCLA. My research is in statistical machine learning, with a focus on developing and analyzing nonconvex optimization algorithms for machine learning to understand large-scale, dynamic, complex and heterogeneous data, and building the theoretical foundations of deep learning. I am leading the Statistical Machine Learning Lab. I received my Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014.

I am very fortunate to have received a couple of awards for my work, including Yahoo! Academic Career Enhancement Award (2015), NSF CAREER Award (2017), Salesforce Deep Learning Research Award (2018), Adobe Data Science Research Award (2018), Simons Berkeley Research Fellowship (2019), and AWS Machine Learning Research Award (2020). Here is my latest CV.

News and Annoucement

• [March 28, 2020] We just launched a project using machine learning and AI to combat Covid-19!

  We just launched a project (https://t.co/33PBBiuUIr) using machine learning to better understand the spread of COVID-19, to facilitate informed decisions by policy makers, and to better allocate the medical resources. More results are coming soon! @CS_UCLA @UCLAengineering @UCLA pic.twitter.com/MbZ2wHLlwK

  — Quanquan Gu (@QuanquanGu) April 11, 2020

Recent Services

• Area Chair for ICML (2019, 2020), NeurIPS (2019), ICLR (2020, 2021), AAAI (2020, 2021), IJCAI (2021), AISTATS (2020, 2021)

• Senior Program Commitee Member for IJCAI (2019, 2020), ACML (2019)

Recent Research Highlight

• Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
  Dongruo Zhou, Jiafan He and Quanquan Gu, arXiv:2006.13165, 2020.

• A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
  Zixiang Chen, Yuan Cao, Quanquan Gu and Tong Zhang, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 33, 2020. [arXiv]

• How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
  Zixiang Chen*, Yuan Cao*, Difan Zou* and Quanquan Gu, arXiv:1911.12360, 2019.

• Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
  Yuan Cao and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 32, Vancouver, Canada, 2019. Spotlight presentation [arXiv]

• An Improved Analysis of Training Over-parameterized Deep Neural Networks
  Difan Zou and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 32, Vancouver, Canada, 2019. [arXiv]

• Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
  Difan Zou*, Yuan Cao*, Dongruo Zhou and Quanquan Gu, Accepted by the Machine Learning Journal (MLJ), 2019. [arXiv]

• Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
  Dongruo Zhou and Quanquan Gu, in Proc. of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, 2019. [arXiv]

• Stochastic Nested Variance Reduction for Nonconvex Optimization
  Dongruo Zhou, Pan Xu and Quanquan Gu, In Proc. of Advances in Neural Information Processing Systems (NeurIPS) 31, Montréal, Canada, 2018. Spotlight presentation [arXiv]

Contact

• Address: EVI 282, 404 Westwood Plaza, Los Angeles, CA 90095

• Email: qgu at cs dot ucla dot edu

• Follow @QuanquanGu

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