Dr. Nanpeng Yu received his B.S. in Electrical Engineering from Tsinghua University, Beijing, China, in 2006. Dr. Yu received his M.S. degrees in Electrical Engineering and Economics and Ph.D. degree from Iowa State University in 2010. Before joining University of California, Riverside, Dr. Yu was a senior power system planner and project manager at Southern California Edison from Jan, 2011 to July 2014.
Currently, he is an associate professor and vice chair of Electrical and Computer Engineering at the University of California, Riverside, CA. Dr. Yu is the recipient of the Regents Faculty Fellowship and Regents Faculty Development award from University of California. He received multiple best paper and prize paper awards from the IEEE Power and Energy Society General Meetings, IEEE Power and Energy Society Grand International Conference and Exposition Asia and the Second International Conference on Green Communications, Computing and Technologies.
Dr. Yu is the director of Energy, Economics, and Environment Research Center at UC Riverside. Dr. Yu is also a cooperating faculty member of department of computer science and engineering and department of Statistics. He currently serves as the chair of distribution system operation and planning subcommittee of IEEE Power and Energy Society and the chair for IEEE Power and Energy Society Working group of data-driven modeling, monitoring and control for Power Distribution Networks. Dr. Yu currently serves as the associate editor for IEEE Transactions on Smart Grid and IEEE Power Engineering Letters.
Title of Speech: Predicting COVID-19 Transmission in Southern California with Machine Learning Methods
Abstract: The COVID-19 pandemic has posed significant challenges to global health, economies, and social structures since its emergence in late 2019. Southern California, a region with unique characteristics and diverse communities, has experienced difficulties in controlling the virus's spread. Understanding the factors that influence COVID-19 transmission in Southern California is crucial for informing public health policies and mitigating the virus's impact on the communities. In this study, we collected weekly zip code level data from March to December 2020 in Southern California and applied various machine learning algorithms, such as graph neural networks (GNN), multi-layer perceptrons (MLP), and gradient boosting trees (XGBoost), to model the spread of COVID-19. Our main contributions include the development of various COVID-19 forecasting models that incorporate socioeconomically significant variables and zip code level mobility data, coupled with variable impact assessments to provide valuable insights for informing future public health policies and strategies. Our predictive model not only attains high levels of accuracy in forecasting but also facilitates the understanding of how each variable contributes to the final outcome. These findings have the potential to enhance our understanding of COVID-19 transmission patterns and inform targeted mitigation strategies in the region.