时 间：5月7日下午 16：00-18：00

腾讯会议号：689 207 902

报告人：瑞士洛桑联邦理工学院（EPFL）Daniel Kuhn教授

报告人简介：

Daniel Kuhn is full Professor of Operations Research at the College of Management of Technology atÉcole polytechnique fédérale de Lausanne, Switzerland (EPFL), where he holds the Chair of Risk Analytics and Optimization (RAO). Before joining EPFL, he was a faculty member at Imperial College London (2007–2013) and a postdoctoral researcher at Stanford University (2005–2006). He received a Ph.D. in Economics from the University of St. Gallen in 2004 and an M.Sc. in Theoretical Physics from ETH Zürich in 1999. His research interests revolve around robust optimization and stochastic programming. He serves as the area editor for continuous optimization for Operations Research and as an associate editor for several other journals including Management Science, Mathematical Programming, Mathematics of Operations Research and Operations Research Letters.

报告内容简介：

This talk highlights some of the main pitfalls that have to be circumnavigated when dealing with optimization problems affected by uncertainty. Emphasis will be put on high-level concepts, thought-provoking examples and insightful experiments instead of mathematical theory. This work is primarily application-driven, the main application areas being engineered systems, machine learning, business analytics and finance.

（承办：管理工程系、科研与学术交流中心）