Daniel Kuhn
daniel.kuhn@epfl.ch +41 21 693 00 46 https://www.epfl.ch/labs/rao/
Citizenship: Swiss
EPFL CDM MTEI RAO
ODY 1 01.2 (Odyssea)
Station 5
CH-1015 Lausanne
+41 21 693 00 46
+41 21 693 01 22
Office:
ODY 1 04
EPFL
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CDM
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MTEI
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RAO
Web site: Web site: https://www.epfl.ch/labs/rao/
EPFL CDM-DIR
ODY 2 04 (Odyssea)
Station 5
CH-1015 Lausanne
Web site: Web site: https://www.epfl.ch/schools/cdm/
Fields of expertise
- Decision-making under uncertainty
- Stochastic programming and robust optimization
- Data-driven optimization
- Stochastic programming and robust optimization
- Data-driven optimization
Biography
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 and the area editor for continuous optimization for Operations Research.
Publications
Infoscience publications
Main publications
2023
Frequency Regulation with Storage: On Losses and Profits
2023-06-05. DOI : 10.48550/arXiv.2306.02987.Distributionally Robust Linear Quadratic Control
2023. DOI : 10.48550/arXiv.2305.17037.Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets
2023. DOI : 10.48550/arXiv.2305.19004.End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
2023. 40th International Conference on Machine Learning, Honolulu, Hawaii, USA, July 23-29 2023.New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
2023. DOI : 10.48550/arXiv.2303.03900.Stability Verification of Neural Network Controllers using Mixed-Integer Programming
IEEE Transactions on Automatic Control. 2023. DOI : 10.1109/TAC.2023.3283213.On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls
Operations Research Letters. 2023. DOI : 10.1016/j.orl.2023.02.008.Efficient Learning of a Linear Dynamical System with Stability Guarantees
IEEE Transactions on Automatic Control. 2023. DOI : 10.1109/TAC.2022.3213770.Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
Mathematical Programming. 2023. DOI : 10.1007/s10107-022-01856-x.A Planner-Trader Decomposition for Multi-Market Hydro Scheduling
Operations Research. 2023. DOI : 10.1287/opre.2023.2456.Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
Mathematics of Operations Research. 2023. DOI : 10.1287/moor.2021.1176.2022
Distributionally Robust Optimal Allocation with Costly Verification
2022.Small Errors in Random Zeroth-Order Optimization are Imaginary
2022.Metrizing Fairness
2022.Vehicle-to-Grid for Reliable Frequency Regulation
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8542.Discrete Optimal Transport with Independent Marginals is #P-Hard
SIAM Journal on Optimization. 2022.Topological Linear System Identification via Moderate Deviations Theory
IEEE Control Systems Letters. 2022. DOI : 10.1109/LCSYS.2021.3072814.On Linear Optimization over Wasserstein Balls
Mathematical Programming. 2022. DOI : 10.1007/s10107-021-01673-8.Data-Driven Chance Constrained Programs over Wasserstein Balls
Operations Research. 2022. DOI : 10.1287/opre.2022.2330.Robust Multidimensional Pricing: Separation without Regret
Mathematical Programming. 2022. DOI : 10.1007/s10107-021-01615-4.Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
Operations Research. 2022. DOI : 10.1287/opre.2020.2076.Scenario Reduction Revisited: Fundamental Limits and Guarantees
Mathematical Programming. 2022. DOI : 10.1007/s10107-018-1269-1.2021
On Topological Equivalence in Linear Quadratic Optimal Control
2021. 2021 European Control Conference (ECC), Rotterdam, Netherlands, June 29 - July 2, 2021. p. 2002-2007. DOI : 10.23919/ECC54610.2021.9654863.Mean-Covariance Robust Risk Measurement
2021.Robust Generalization despite Distribution Shift via Minimum Discriminating Information
2021. 35th Conference on Neural Information Processing Systems (NeurIPS), Virtual, December 7-10, 2021.Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
2021. 38th International Conference on Machine Learning (ICML 2021), Virtual, July 18-24, 2021. p. 7168-7179.Distributionally Robust Optimization with Markovian Data
2021. 38th International Conference on Machine Learning, Virtual, July 18-24, 2021. p. 6493-6503.Mathematical Foundations of Robust and Distributionally Robust Optimization
2021.A General Framework for Optimal Data-Driven Optimization
2021.A Statistical Test for Probabilistic Fairness
2021. ACM Conference on Fairness, Accountability, and Transparency, March 3-10, 2021. DOI : 10.1145/3442188.3445927.Energy and Reserve Dispatch with Distributionally Robust Joint Chance Constraints
Operations Research Letters. 2021. DOI : 10.1016/j.orl.2021.01.012.From Data to Decisions: Distributionally Robust Optimization is Optimal
Management Science. 2021. DOI : 10.1287/mnsc.2020.3678.2020
Scalable Stochastic Optimization: Scenario Reduction with Guarantees
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-10298.Distributional Robustness in Mechanism Design
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7442.Regret Minimization and Separation in Multi-Bidder Multi-Item Auctions
2020.Wasserstein Distributionally Robust Learning
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-10012.Reliable Frequency Regulation through Vehicle-to-Grid: From EU Legislation to Robust Optimization
2020.Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms
Mathematical Programming. 2020. DOI : 10.1007/s10107-019-01429-5.Distributionally Robust Mechanism Design
Management Science. 2020. DOI : 10.1287/mnsc.2018.3219.2019
Adversarial Analytics
Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9731.Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
2019. 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 8-14, 2019.Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
2019. Neural Information Processing Systems, Vancouver, Canada, December 8-14, 2019.Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Operations Research & Management Science in the Age of Analytics; 2019. p. 130-166.Regularization via Mass Transportation
Journal of Machine Learning Research. 2019.Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization
SIAM Journal on Optimization. 2019. DOI : 10.1137/17M1150670."Dice"-sion Making under Uncertainty: When Can a Random Decision Reduce Risk?
Management Science. 2019. DOI : 10.1287/mnsc.2018.3108.The Decision Rule Approach to Optimisation under Uncertainty: Methodology and Applications
Computational Management Science. 2019. DOI : 10.1007/s10287-018-0338-5.2018
Wasserstein Distributionally Robust Kalman Filtering
2018. Neural Information Processing Systems, Montréal, Canada, December 2-8, 2018.On Risk Reduction in Kelly Betting Using the Conservative Expected Value
2018. 57th IEEE Conference on Decision and Control, Miami Beach, Florida, USA, December 17-19, 2018. p. 5801-5806. DOI : 10.1109/CDC.2018.8619186.From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming
SIAM Journal on Optimization. 2018. DOI : 10.1137/17M1133087.Decision Rule Bounds for Two-Stage Stochastic Bilevel Programs
SIAM Journal on Optimization. 2018. DOI : 10.1137/16M1098486.Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls
Operations Research. 2018. DOI : 10.1287/opre.2017.1698.Chebyshev Inequalities for Products of Random Variables
Mathematics of Operations Research. 2018. DOI : 10.1287/moor.2017.0888.Data-Driven Inverse Optimization with Incomplete Information
Mathematical Programming. 2018. DOI : 10.1007/s10107-017-1216-6.Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations
Mathematical Programming. 2018. DOI : 10.1007/s10107-017-1172-1.2017
Optimal Financial Decision Making Under Uncertainty
Optimal Financial Decision Making under Uncertainty; Springer International Publishing, 2017. p. 255-290.Ambiguous Joint Chance Constraints under Mean and Dispersion Information
Operations Research. 2017. DOI : 10.1287/opre.2016.1583.2016
A linear programming approach to the optimization of residential energy systems
Journal of Energy Storage. 2016. DOI : 10.1016/j.est.2016.04.009.Dimensionality Reduction in Dynamic Optimization under Uncertainty
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7235.K-Adaptability in Two-Stage Distributionally Robust Binary Programming
Operations Research Letters. 2016. DOI : 10.1016/j.orl.2015.10.006.A Comment on “Computational Complexity of Stochastic Programming Problems”
Mathematical Programming. 2016. DOI : 10.1007/s10107-015-0958-2.Robust Growth-Optimal Portfolios
Management Science. 2016. DOI : 10.1287/mnsc.2015.2228.Distributionally Robust Control of Constrained Stochastic Systems
IEEE Transactions on Automatic Control. 2016. DOI : 10.1109/TAC.2015.2444134.Generalized Gauss Inequalities via Semidefinite Programming
Mathematical Programming. 2016. DOI : 10.1007/s10107-015-0878-1.2015
Financial Optimization: Optimization Paradigms and Financial Planning under Uncertainty
OR Spectrum. 2015. DOI : 10.1007/s00291-015-0406-y.Distributionally Robust Logistic Regression
2015. Neural Information Processing Systems, Montréal, Canada, December 7-12, 2015.Interdiction Games on Markovian PERT Networks
Management Science. 2015. DOI : 10.1287/mnsc.2014.1973.A Distributionally Robust Perspective on Uncertainty Quantification and Chance Constrained Programming
Mathematical Programming. 2015. DOI : 10.1007/s10107-015-0896-z.The Stochastic Time-Constrained Net Present Value Problem
Handbook on Project Management and Scheduling Vol. 2; Springer International Publishing, 2015. p. 753-780.K-Adaptability in Two-Stage Robust Binary Programming
Operations Research. 2015. DOI : 10.1287/opre.2015.1392.Generalized Decision Rule Approximations for Stochastic Programming via Liftings
Mathematical Programming. 2015. DOI : 10.1007/s10107-014-0789-6.Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions
Mathematical Programming. 2015. DOI : 10.1007/s10107-014-0776-y.2014
Distributionally Robust Convex Optimization
Operations Research. 2014. DOI : 10.1287/opre.2014.1314.2013
Robust Data-Driven Dynamic Programming
2013. Neural Information Processing Systems, Lake Tahoe, USA, December 2013.Distributionally Robust Joint Chance Constraints with Second-Order Moment Information
Mathematical Programming. 2013. DOI : 10.1007/s10107-011-0494-7.Worst-Case Value at Risk of Nonlinear Portfolios
Management Science. 2013. DOI : 10.1287/mnsc.1120.1615.Robust Markov Decision Processes
Mathematics of Operations Research. 2013. DOI : 10.1287/moor.1120.0566.A Polynomial-Time Solution Scheme for Quadratic Stochastic Programs
Journal of Optimization Theory and Applications. 2013. DOI : 10.1007/s10957-012-0264-6.2012
Guest Editorial: Special Issue on Optimal Decision Making under Uncertainty
Computational Management Science. 2012. DOI : 10.1007/s10287-011-0136-9.Risk-averse shortest path problems
2012. 2012 IEEE 51st Annual Conference on Decision and Control (CDC), Maui, HI, USA, December 10-13, 2012. p. 2533-2538. DOI : 10.1109/CDC.2012.6426188.Robust resource allocations in temporal networks
Mathematical Programming. 2012. DOI : 10.1007/s10107-011-0478-7.Multi-resource allocation in stochastic project scheduling
Annals of Operations Research. 2012. DOI : 10.1007/s10479-008-0486-z.A constraint sampling approach for multi-stage robust optimization
Automatica. 2012. DOI : 10.1016/j.automatica.2011.12.002.Robust Software Partitioning with Multiple Instantiation
INFORMS Journal on Computing. 2012. DOI : 10.1287/ijoc.1110.0467.Multistage stochastic portfolio optimisation in deregulated electricity markets using linear decision rules
European Journal of Operational Research. 2012. DOI : 10.1016/j.ejor.2011.08.001.Polynomial Approximations for Continuous Linear Programs
SIAM Journal on Optimization. 2012. DOI : 10.1137/110822992.2011
An Efficient Method to Estimate the Suboptimality of Affine Controllers
IEEE Transactions on Automatic Control. 2011. DOI : 10.1109/TAC.2011.2139390.Guest Editorial: Special Issue on Computational Finance
Computational Management Science. 2011. DOI : 10.1007/s10287-009-0112-9.SQPR: Stream query planning with reuse
2011. 2011 IEEE International Conference on Data Engineering (ICDE 2011), Hannover, Germany, 11-16 04 2011. p. 840-851. DOI : 10.1109/ICDE.2011.5767851.Welfare-Maximizing Correlated Equilibria with an Application to Wireless Communication
2011. 18th IFAC World Congress, Università Cattolica del Sacro Cuore, Milano, Italy, August 2011. p. 8920-8925. DOI : 10.3182/20110828-6-IT-1002.02982.Hedging Electricity Swing Options in Incomplete Markets
2011. 18th IFAC World Congress, Università Cattolica del Sacro Cuore, Milano, Italy, August 2011. p. 846-853. DOI : 10.3182/20110828-6-IT-1002.03528.A scenario approach for estimating the suboptimality of linear decision rules in two-stage robust optimization
2011. 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011), Orlando, FL, USA, 12-15 December 2011. p. 7386-7391. DOI : 10.1109/CDC.2011.6161342.Decision rules for information discovery in multi-stage stochastic programming
2011. 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011), Orlando, FL, USA, December 12-15, 2011. p. 7368-7373. DOI : 10.1109/CDC.2011.6161382.Scenario-free stochastic programming with polynomial decision rules
2011. 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011), Orlando, FL, USA, December 12-15, 2011. p. 7806-7812. DOI : 10.1109/CDC.2011.6161150.Barycentric Bounds in Stochastic Programming: Theory and Application
Stochastic Programming: The State of the Art, In Honor of George B. Dantzig; New York, NY: Springer New York, 2011. p. 67-96.Robust portfolio optimization with derivative insurance guarantees
European Journal of Operational Research. 2011. DOI : 10.1016/j.ejor.2010.09.027.Primal and dual linear decision rules in stochastic and robust optimization
Mathematical Programming. 2011. DOI : 10.1007/s10107-009-0331-4.2010
A cutting-plane method for Mixed-Logical Semidefinite Programs with an application to multi-vehicle robust path planning
2010. 2010 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA, December 15-17, 2010. p. 1360-1365. DOI : 10.1109/CDC.2010.5717988.Linearly Adjustable International Portfolios
2010. ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics, Rhodes, Greece, September 19–25, 2010. p. 338-341. DOI : 10.1063/1.3498469.Maximizing the net present value of a project under uncertainty
European Journal of Operational Research. 2010. DOI : 10.1016/j.ejor.2009.05.045.Teaching & PhD
Teaching
Management of Technology
PhD Students
Jongeneel Wouter, Li Mengmeng, Rychener Yves, Schwan Roland, Sen Buse, Taskesen Bahar, Yang Tianshu,Past EPFL PhD Students
Kocyigit Cagil , Lauinger Dirk , Nguyen Viet Anh , Rujeerapaiboon Napat , Schindler Kilian , Shafieezadeh Abadeh Soroosh ,Courses
Convex optimization
This course introduces the theory and application of modern convex optimization from an engineering perspective.
Optimal decision making
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.