主讲人：
Haoxiang Yang，Assistant Professor, School of Data Science, CUHK-Shenzhen
主持老师：
（北大经院）王熙
参与老师：
（北大经院）王一鸣、刘蕴霆、王法、李少然、巩爱博
（北大国发院）黄卓、张俊妮、常晋源
时间：
2026年11月20日（周五）
10：00-11:30
地点（线下）：
北京大学经济学院302会议室
主讲人简介：
Haoxiang Yang is an assistant professor at the School of Data Science, the Chinese University of Hong Kong, Shenzhen (CUHK-SZ). He graduated with a PhD degree from the Department of Industrial Engineering and Management Sciences at Northwestern University in 2019. Prior to joining CUHK-SZ, he worked in Los Alamos National Laboratory between 2019 and 2021 as a postdoctoral research associate. Haoxiang's research focuses on the theory of optimization under uncertainty, specifically in stochastic programming and robust optimization, integer programming and nonlinear programming, with its applications in energy systems, supply chain and logistics, disaster relief and sports analytics. His research has been awarded the IISE Transactions Operations Engineering & Analytics Best Application Paper and Asian Pacific Operations Research Society (APORS) Young Researcher Best Paper. Haoxiang serves on the Committee on Stochastic Programming (COSP), the governing board of the Stochastic Programming Society.
报告摘要：
New technology introduction, policy changes and environmental challenges create new optimization problems for power system planning and operations. This talk presents optimization under uncertainty methods for three applications. First, stochastic optimization coordinates preventive power shutoffs and grid operations to balance wildfire risk and service interruptions. Second, robust optimization guides electric vehicle charging infrastructure expansion while accounting for how station openings change charging demand. Third, decision-focused learning connects price forecasts with wind power commitments to improve settlement profit. Through computational studies and industry collaboration, these applications demonstrate how modeling uncertainty, capturing the effects of decisions on demand, and integrating learning with optimization can improve the reliability and economic performance of power systems.
