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 04 (Odyssea)
Station 5
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/
+41 21 693 00 46
EPFL
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CDM
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CDM-DIR
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CDM-DI
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.
Publications
Infoscience publications
Publications
2025
Distributionally Robust Optimal Allocation with Costly Verification
Operations Research. 2025.2024
Distributionally Robust Optimization
Acta Numerica. 2024.Frequency Regulation with Storage: On Losses and Profits
European Journal of Operational Research. 2024. DOI : 10.1016/j.ejor.2024.03.022.Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources
2024Regret Minimization and Separation in Multi-Bidder Multi-Item Auctions
INFORMS Journal on Computing. 2024. DOI : 10.1287/ijoc.2022.0275.A Pareto Dominance Principle for Data-Driven Optimization
Operations Research. 2024. DOI : 10.1287/opre.2021.0609.A Large Deviations Perspective on Policy Gradient Algorithms
2024. 6th Annual Learning for Dynamics & Control Conference, Oxford, UK, 2024-07-15 - 2024-07-17.Small Errors in Random Zeroth-Order Optimization are Imaginary
SIAM Journal on Optimization. 2024. DOI : 10.1137/22M1510261.Data-Driven Chance Constrained Programs over Wasserstein Balls
Operations Research. 2024. DOI : 10.1287/opre.2022.2330.Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints
Manufacturing & Service Operations Management. 2024. DOI : 10.1287/msom.2022.0154.Reliable data-driven decision-making through optimal transport
Lausanne, EPFL, 2024. DOI : 10.5075/epfl-thesis-10134.Stability: a search for structure
Lausanne, EPFL, 2024. DOI : 10.5075/epfl-thesis-10550.A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set
2024A Planner-Trader Decomposition for Multi-Market Hydro Scheduling
Operations Research. 2024. DOI : 10.1287/opre.2023.2456.2023
Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
Mathematics of Operations Research. 2023. DOI : 10.1287/moor.2021.1176.On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls
Operations Research Letters. 2023. DOI : 10.1016/j.orl.2023.02.008.Distributionally Robust Linear Quadratic Control
2023. 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, December 10-16, 2023. DOI : 10.48550/arXiv.2305.17037.Efficient Learning of a Linear Dynamical System with Stability Guarantees
IEEE Transactions on Automatic Control. 2023. DOI : 10.1109/TAC.2022.3213770.New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
2023End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
2023. 40th International Conference on Machine Learning, Honolulu, Hawaii, USA, July 23-29 2023. DOI : 10.48550/arXiv.2306.04174.A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principle
Operations Research. 2023. DOI : 10.1287/opre.2021.0268.Contextual Stochastic Bilevel Optimization
2023. 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, December 10-16, 2023. DOI : 10.48550/arXiv.2310.18535.Discrete Optimal Transport with Independent Marginals is #P-Hard
SIAM Journal on Optimization. 2023. DOI : 10.1137/22M1482044.Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
Mathematical Programming. 2023. DOI : 10.1007/s10107-022-01856-x.Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets
2023PIQP: A Proximal Interior-Point Quadratic Programming Solver
2023 62Nd Ieee Conference On Decision And Control, Cdc. 2023. 62nd IEEE Conference on Decision and Control (CDC), Singapore, SINGAPORE, DEC 13-15, 2023. p. 1088 - 1093. DOI : 10.1109/CDC49753.2023.10383915.Stability Verification of Neural Network Controllers using Mixed-Integer Programming
IEEE Transactions on Automatic Control. 2023. DOI : 10.1109/TAC.2023.3283213.2022
Scenario Reduction Revisited: Fundamental Limits and Guarantees
Mathematical Programming. 2022. DOI : 10.1007/s10107-018-1269-1.On Linear Optimization over Wasserstein Balls
Mathematical Programming. 2022. DOI : 10.1007/s10107-021-01673-8.Vehicle-to-Grid for Reliable Frequency Regulation
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8542.Metrizing Fairness
2022Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
Operations Research. 2022. DOI : 10.1287/opre.2020.2076.Robust Multidimensional Pricing: Separation without Regret
Mathematical Programming. 2022. DOI : 10.1007/s10107-021-01615-4.Topological Linear System Identification via Moderate Deviations Theory
IEEE Control Systems Letters. 2022. DOI : 10.1109/LCSYS.2021.3072814.2021
Distributionally Robust Optimization with Markovian Data
Proceedings of the 38th International Conference on Machine Learning. 2021. 38th International Conference on Machine Learning, Virtual, July 18-24, 2021. p. 6493 - 6503.Energy and Reserve Dispatch with Distributionally Robust Joint Chance Constraints
Operations Research Letters. 2021. DOI : 10.1016/j.orl.2021.01.012.Robust Generalization despite Distribution Shift via Minimum Discriminating Information
2021. 35th Conference on Neural Information Processing Systems (NeurIPS), Virtual, December 7-10, 2021.From Data to Decisions: Distributionally Robust Optimization is Optimal
Management Science. 2021. DOI : 10.1287/mnsc.2020.3678.On Topological Equivalence in Linear Quadratic Optimal Control
2021 European Control Conference (ECC). 2021. 2021 European Control Conference (ECC), Rotterdam, Netherlands, June 29 - July 2, 2021. p. 2002 - 2007. DOI : 10.23919/ECC54610.2021.9654863.A Statistical Test for Probabilistic Fairness
2021. ACM Conference on Fairness, Accountability, and Transparency, March 3-10, 2021. DOI : 10.1145/3442188.3445927.Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
International Conference On Machine Learning. 2021. 38th International Conference on Machine Learning (ICML 2021), Virtual, July 18-24, 2021. p. 7168 - 7179.Mean-Covariance Robust Risk Measurement
20212020
Wasserstein Distributionally Robust Learning
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-10012.Distributional Robustness in Mechanism Design
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7442.Scalable Stochastic Optimization: Scenario Reduction with Guarantees
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-10298.Distributionally Robust Mechanism Design
Management Science. 2020. DOI : 10.1287/mnsc.2018.3219.Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms
Mathematical Programming. 2020. DOI : 10.1007/s10107-019-01429-5.2019
The Decision Rule Approach to Optimisation under Uncertainty: Methodology and Applications
Computational Management Science. 2019. DOI : 10.1007/s10287-018-0338-5.Adversarial Analytics
Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9731.Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Operations Research & Management Science in the Age of Analytics; 2019. p. 130 - 166.DOI : 10.1287/educ.2019.0198.
Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization
SIAM Journal on Optimization. 2019. DOI : 10.1137/17M1150670.Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
Advances In Neural Information Processing Systems 32 (Nips 2019). 2019. Neural Information Processing Systems, Vancouver, Canada, December 8-14, 2019.Regularization via Mass Transportation
Journal of Machine Learning Research. 2019.Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
Advances In Neural Information Processing Systems 32 (Nips 2019), 32. 2019. 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 8-14, 2019."Dice"-sion Making under Uncertainty: When Can a Random Decision Reduce Risk?
Management Science. 2019. DOI : 10.1287/mnsc.2018.3108.2018
Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations
Mathematical Programming. 2018. DOI : 10.1007/s10107-017-1172-1.Chebyshev Inequalities for Products of Random Variables
Mathematics of Operations Research. 2018. DOI : 10.1287/moor.2017.0888.Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls
Operations Research. 2018. DOI : 10.1287/opre.2017.1698.From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming
SIAM Journal on Optimization. 2018. DOI : 10.1137/17M1133087.Wasserstein Distributionally Robust Kalman Filtering
NIPS Proceedings. 2018. Neural Information Processing Systems, Montréal, Canada, December 2-8, 2018.Decision Rule Bounds for Two-Stage Stochastic Bilevel Programs
SIAM Journal on Optimization. 2018. DOI : 10.1137/16M1098486.On Risk Reduction in Kelly Betting Using the Conservative Expected Value
2018 IEEE Conference on Decision and Control (CDC). 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.Data-Driven Inverse Optimization with Incomplete Information
Mathematical Programming. 2018. DOI : 10.1007/s10107-017-1216-6.2017
Optimal Financial Decision Making Under Uncertainty
Optimal Financial Decision Making under Uncertainty; Springer International Publishing, 2017. p. 255 - 290.DOI : 10.1007/978-3-319-41613-7_11.
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.Distributionally Robust Control of Constrained Stochastic Systems
IEEE Transactions on Automatic Control. 2016. DOI : 10.1109/TAC.2015.2444134.A Comment on “Computational Complexity of Stochastic Programming Problems”
Mathematical Programming. 2016. DOI : 10.1007/s10107-015-0958-2.Generalized Gauss Inequalities via Semidefinite Programming
Mathematical Programming. 2016. DOI : 10.1007/s10107-015-0878-1.K-Adaptability in Two-Stage Distributionally Robust Binary Programming
Operations Research Letters. 2016. DOI : 10.1016/j.orl.2015.10.006.Robust Growth-Optimal Portfolios
Management Science. 2016. DOI : 10.1287/mnsc.2015.2228.Dimensionality Reduction in Dynamic Optimization under Uncertainty
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7235.2015
Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions
Mathematical Programming. 2015. DOI : 10.1007/s10107-014-0776-y.Generalized Decision Rule Approximations for Stochastic Programming via Liftings
Mathematical Programming. 2015. DOI : 10.1007/s10107-014-0789-6.K-Adaptability in Two-Stage Robust Binary Programming
Operations Research. 2015. DOI : 10.1287/opre.2015.1392.A Distributionally Robust Perspective on Uncertainty Quantification and Chance Constrained Programming
Mathematical Programming. 2015. DOI : 10.1007/s10107-015-0896-z.Distributionally Robust Logistic Regression
NIPS Proceedings. 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.Financial Optimization: Optimization Paradigms and Financial Planning under Uncertainty
OR Spectrum. 2015. DOI : 10.1007/s00291-015-0406-y.The Stochastic Time-Constrained Net Present Value Problem
Handbook on Project Management and Scheduling Vol. 2; Springer International Publishing, 2015. p. 753 - 780.DOI : 10.1007/978-3-319-05915-0_5.
2014
Distributionally Robust Convex Optimization
Operations Research. 2014. DOI : 10.1287/opre.2014.1314.2013
Distributionally Robust Joint Chance Constraints with Second-Order Moment Information
Mathematical Programming. 2013. DOI : 10.1007/s10107-011-0494-7.A Polynomial-Time Solution Scheme for Quadratic Stochastic Programs
Journal of Optimization Theory and Applications. 2013. DOI : 10.1007/s10957-012-0264-6.Robust Data-Driven Dynamic Programming
NIPS Proceedings 26. 2013. Neural Information Processing Systems, Lake Tahoe, USA, December 2013.Robust Markov Decision Processes
Mathematics of Operations Research. 2013. DOI : 10.1287/moor.1120.0566.Worst-Case Value at Risk of Nonlinear Portfolios
Management Science. 2013. DOI : 10.1287/mnsc.1120.1615.2012
Multi-resource allocation in stochastic project scheduling
Annals of Operations Research. 2012. DOI : 10.1007/s10479-008-0486-z.Guest Editorial: Special Issue on Optimal Decision Making under Uncertainty
Computational Management Science. 2012. DOI : 10.1007/s10287-011-0136-9.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.Robust Software Partitioning with Multiple Instantiation
INFORMS Journal on Computing. 2012. DOI : 10.1287/ijoc.1110.0467.Polynomial Approximations for Continuous Linear Programs
SIAM Journal on Optimization. 2012. DOI : 10.1137/110822992.Risk-averse shortest path problems
2012 IEEE 51st IEEE Conference on Decision and Control (CDC). 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.A constraint sampling approach for multi-stage robust optimization
Automatica. 2012. DOI : 10.1016/j.automatica.2011.12.002.Robust resource allocations in temporal networks
Mathematical Programming. 2012. DOI : 10.1007/s10107-011-0478-7.2011
Primal and dual linear decision rules in stochastic and robust optimization
Mathematical Programming. 2011. DOI : 10.1007/s10107-009-0331-4.Hedging Electricity Swing Options in Incomplete Markets
Proceedings of the 18th IFAC World Congress. 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.Scenario-free stochastic programming with polynomial decision rules
IEEE Conference on Decision and Control and European Control Conference. 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.Welfare-Maximizing Correlated Equilibria with an Application to Wireless Communication
Proceedings of the 18th IFAC World Congress. 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.Decision rules for information discovery in multi-stage stochastic programming
IEEE Conference on Decision and Control and European Control Conference. 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.SQPR: Stream query planning with reuse
2011 IEEE 27th International Conference on Data Engineering. 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.Guest Editorial: Special Issue on Computational Finance
Computational Management Science. 2011. DOI : 10.1007/s10287-009-0112-9.Robust portfolio optimization with derivative insurance guarantees
European Journal of Operational Research. 2011. DOI : 10.1016/j.ejor.2010.09.027.An Efficient Method to Estimate the Suboptimality of Affine Controllers
IEEE Transactions on Automatic Control. 2011. DOI : 10.1109/TAC.2011.2139390.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.DOI : 10.1007/978-1-4419-1642-6_5.
A scenario approach for estimating the suboptimality of linear decision rules in two-stage robust optimization
IEEE Conference on Decision and Control and European Control Conference. 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.2011.6161342.2010
A cutting-plane method for Mixed-Logical Semidefinite Programs with an application to multi-vehicle robust path planning
49th IEEE Conference on Decision and Control (CDC). 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.Maximizing the net present value of a project under uncertainty
European Journal of Operational Research. 2010. DOI : 10.1016/j.ejor.2009.05.045.Linearly Adjustable International Portfolios
AIP Conference Proceedings. 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.Analysis of the rebalancing frequency in log-optimal portfolio selection
Quantitative Finance. 2010. DOI : 10.1080/14697680802629400.2009
An Information-Based Approximation Scheme for Stochastic Optimization Problems in Continuous Time
Mathematics of Operations Research. 2009. DOI : 10.1287/moor.1080.0369.Rapid Design Space visualisation through hardware/software partitioning
2009 5th Southern Conference on Programmable Logic (SPL). 2009. 2009 5th Southern Conference on Programmable Logic (SPL), São Carlos, Brazil, 1-3 04 2009. p. 159 - 164. DOI : 10.1109/SPL.2009.4914913.Valuation of electricity swing options by multistage stochastic programming
Automatica. 2009. DOI : 10.1016/j.automatica.2008.11.022.Convergent Bounds for Stochastic Programs with Expected Value Constraints
Journal of Optimization Theory and Applications. 2009. DOI : 10.1007/s10957-008-9476-1.Dynamic Mean-Variance Portfolio Analysis under Model Risk
Journal of Computational Finance. 2009. DOI : 10.21314/JCF.2009.202.2008
Aggregation and discretization in multistage stochastic programming
Mathematical Programming. 2008. DOI : 10.1007/s10107-006-0048-6.Bound-based decision rules in multistage stochastic programming
Kybernetika. 2008.A Stochastic Programming Approach for QoS-Aware Service Composition
2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID). 2008. 2008 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid), Lyon, France, 19-22 05 2008. p. 226 - 233. DOI : 10.1109/CCGRID.2008.40.Stochastic Optimization of Investment Planning Problems in the Electric Power Industry
Energy Systems Engineering; Weinheim: Wiley-VCH, 2008. p. 215 - 230.DOI : 10.1002/9783527631292.ch7.
Threshold Accepting Approach to Improve Bound-based Approximations for Portfolio Optimization
Computational Methods in Financial Engineering; Berlin: Springer Verlag, 2008. p. 3 - 26.DOI : 10.1007/978-3-540-77958-2_1.
2005
Numerical Methods to Increase the Value Added
2005. Energy Talks Ossiach '05, Ossiach, Austria, April 13-15, 2005.Swing-Optionen im Elektrizitätsmarkt – Bewertung und optimale Ausübungsstrategien komplexer Stromderivate
2005Stochastische Optimierung im Energiehandel: Entscheidungsunterstützung und Bewertung für das Portfoliomanagement
2005Generalized Bounds for Convex Multistage Stochastic Programs
Berlin: Springer-Verlag.DOI : 10.1007/b138260.
2003
Energy Business and Finance Policy - Parallels in Methodology and Duties
Forschung im Verbund, Schriftenreihe Band 85. 2003. 4. Internationales Energiesymposium, Fuschl, Salzburger Land, Austria, September 25, 2003. p. 124 - 137.A Software Tool for the Valuation and Optimal Exercise of Swing Options
20032002
Umsetzung stochastischer Optimierungsmethoden in der Energiewirtschaft
VDI-Berichte Nr. 1688: IT-Lösungen für die Energiewirtschaft in liberalisierten Märkten. 2002. VDI Tagung "IT-Lösungen für die Energiewirtschaft in liberalisierten Märkten", Schliersee, DE, May 15, 2002. p. 141 - 151.2001
Supercurrents through gated superconductor–normal-metal–superconductor contacts: The Josephson transistor
Physical Review B. 2001. DOI : 10.1103/PhysRevB.63.054520.Teaching & PhD
Teaching
Management of Technology
PhD Students
Li Mengmeng, Nylöf Lars Jakob, Rychener Yves, Schneider Philipp Jürgen, Schwan Roland, Sen Buse, Sharifian Ehsan, Yang Tianshu,Past EPFL PhD Students
Jongeneel Wouter , Kocyigit Cagil , Lauinger Dirk , Nguyen Viet Anh , Rujeerapaiboon Napat , Schindler Kilian , Shafieezadeh Abadeh Soroosh , Taskesen Bahar ,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.