Daniel Kuhn

Daniel Kuhn is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Business / Management department. He holds the Chair of Risk Analytics and Optimization (RAO) and is associated with the College of Management of Technology. His research focuses on data-driven optimization, computational methods for stochastic and robust optimization, and approximation schemes for computational tractability. His work is application-driven, spanning engineered systems, machine learning, business analytics, and finance. Prior to EPFL, he was a faculty member at Imperial College London and a postdoctoral research associate at Stanford University. He holds a PhD in Economics from University of St. Gallen and an MSc in Theoretical Physics from ETH Zurich. He is the editor-in-chief of Mathematical Programming.

Daniel Kuhn is a Professor of Operations Research at EPFL, specializing in stochastic programming, robust optimization, and data-driven optimization. His research focuses on developing mathematical frameworks for decision-making under uncertainty, with an emphasis on distributionally robust optimization and its applications in machine learning and operations. Kuhn's work explores methods to incorporate data into optimization models while ensuring robustness against distributional ambiguity. His publications address topics such as Wasserstein metrics, chance constraints, and regularization techniques for stochastic and robust optimization problems. His research has significant implications for risk management, control systems, and data-driven decision-making.