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 Simons Berkeley Research Fellowship in 2019, Salesforce Deep Learning Research Award and Adobe Data Science Research Award in 2018, NSF CAREER Award in 2017, and Yahoo! Academic Career Enhancement Award in 2015. Here is my latest CV.

News and Annoucement

  • [8/2019] I will give a talk in the workshop on "PDE and Inverse Problem Methods in Machine Learning" at the Insitute of Pure and Applied Math in April, 2020.
  • [8/2019] I will give a talk in the Fudan International Conference on Data Science at Fudan University in December, 2019.
  • [8/2019] I will give a talk in the workshop on "Recent Trends in Clustering and Classification" at Toyota Technological Institute at Chicago in September, 2019.
  • [7/2019] I'm serving as an area chair for AAAI'20 and ICLR'20.

For Prospective Students

I am actively looking for talented graduate and undergraduate students interested in theory of deep learning, adversarial machine learning, nonconvex optimization, reinforcement learning and their applications joining my lab. Please indicate your interest in working with me in your application. Due to time and lab space limit, I don't host visiting students or summer interns.

Recent Research Highlight


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

  • Email: qgu at cs dot ucla dot edu