時 間：5月7日下午 16：00-18：00
騰訊會議號：689 207 902
報告人：瑞士洛桑聯(lián)邦理工學(xué)院（EPFL）Daniel Kuhn教授
報告人簡介：
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.
報告內(nèi)容簡介：
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.
（承辦：管理工程系、科研與學(xué)術(shù)交流中心）
