Online Learning and Decision Making with High-Dimensional Data

Individual-level data have become increasingly accessible in the Internet era, and the growing availability of user-specific data, such as demographics, geographic, medical records, and searching/browsing history, provides decision-makers with unprecedented opportunities to tailor decisions to individual users. Yet, decision-makers’ abilities to use all available information and predict users’ utilities and choices are often impaired by limited samples and/or high computational costs. In this talk, we will apply various state-of-the-art machine-learning algorithms, such as Lasso, MCP, and random projection, to tackle these challenges and demonstrate their effectiveness in real-life applications, such as the recommendation system and the assortment optimization system.

About Mike Wei
Dr. Mike M. Wei is an associate professor of Operations Management in the School of Management at the University at Buffalo. He received his B.B.A. degree in Information Management and Information Systems from Fudan University, M.S. degree in Industrial Engineering and Operations Research from Pennsylvanian State University, and Ph.D. degree in Operations and Manufacturing Management from Olin Business School, Washington University in St. Louis. His research focuses on supply chain management, dynamic pricing, strategic consumer behavior, and high-dimensional machine learning. His work has been published in leading operations management journals and machine learning conferences such as Management Science, Productions and Operations Management, and ICML. He has consulted in consumer electronics, financial, and IT industries.