Statistical Machine Learning


The novel coronavirus disease (COVID-19) has emerged as a global pandemic, and caused over 4.5 million deaths in the world. In this talk, I will introduce our project ( using an epidemic model-guided machine learning approach to understand and forecast the spread of COVID-19 and further facilitate the decision making of the government agencies. In specific, I will introduce our UCLA-SuEIR model, which is a variant of the SEIR model and takes into account the unreported cases of COVID-19. Our model can provide forecasts of COVID-19 confirmed cases and deaths, as well as hospital/ICU bed occupancy at county, state and national level. Our forecasts are being used by the Centers for Disease Control and Prevention (CDC) and California Department of Public Health (CDPH). Various performance evaluations indicate that our model is consistently among the top three forecast models used by CDC.


Quanquan Gu is an Assistant Professor of Computer Science at UCLA and the director of the statistical machine learning lab. His research is in the area of artificial intelligence and 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 and reinforcement learning. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014. He is a recipient of the Yahoo! Academic Career Enhancement Award, NSF CAREER Award, Simons Berkeley Research Fellowship among other industrial research awards. He leads a team at UCLA using machine learning to forecast the spread of COVID-19 ( and their model has been adopted by the U.S. Centers for Disease Control and Prevention and the California Department of Public Health.

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