Daniel Kuhn

Nationality: Swiss

EPFL CDM MTEI RAO
ODY 1 04 (Odyssea)
Station 5
1015 Lausanne

Expertise

- Decision-making under uncertainty
- Stochastic programming and robust optimization
- Data-driven optimization
Daniel Kuhn is Professor of Operations Research at the College of Management of Technology at EPFL, where he holds the Chair of Risk Analytics and Optimization (RAO). His current research interests are focused on data-driven optimization, the development of efficient computational methods for the solution of stochastic and robust optimization problems and the design of approximation schemes that ensure their computational tractability. This work is primarily application-driven, the main application areas being engineered systems, machine learning, business analytics and finance.
Before joining EPFL, Daniel Kuhn was a faculty member in the Department of Computing at Imperial College London (2007-2013) and a postdoctoral research associate in the Department of Management Science and Engineering at Stanford University (2005-2006). He holds a PhD degree in Economics from University of St. Gallen and an MSc degree in Theoretical Physics from ETH Zurich. He is the editor-in-chief of Mathematical Programming.

Publications

Teaching & PhD

PhD Students

Lukas Looser, Ehsan Sharifian, Philipp Schneider, Buse Sen, Tianshu Yang, Jakob Nylöf, Mengmeng Li, Alain Schöbi

Past EPFL PhD Students

Napat Rujeerapaiboon, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Cagil Kocyigit, Kilian Schindler, Dirk Lauinger, Bahar Taskesen, Wouter Jongeneel, Rychener Yves

Past EPFL PhD Students as codirector

Paul Adrianus Van Baal, Roland Schwan

Courses

Convex optimization

MGT-418

This course introduces the theory and application of modern convex optimization from an engineering perspective.

Optimal decision making

MGT-483

This course introduces the theory and applications of optimization. We develop tools and concepts of optimization and decision analysis that enable managers in manufacturing, service operations, marketing, transportation and finance to transform data into insights for making better decisions.