About me

I am an Associate Professor in the Information Systems and Operations Management department at HEC Paris, and a Hi! Paris chair holder. My research focuses on data-driven decision making under parameter uncertainty, with applications in healthcare and algorithmic game theory. I received my Ph.D. in Operations Research from Columbia University in 2021 where my advisors were Prof. Vineet Goyal and Prof. Carri Chan. Before Columbia, I completed my master and undergraduate studies at Ecole polytechnique (France) in 2016, with a major in algorithms and optimization.

Info: Google Scholar / resume

Contact information to be found in my resume

Hiring: I am recruiting a postdoctoral student to work under my supervision at HEC Paris on decision problems at the intersection of optimization, game theory, and reinforcement learning. The funding is for two years (starting Fall 2026) and comes from a grant obtained jointly with Prof. Nian Si (HKUST). More information can be found here.

News:

• Feb 26: one new preprint available, on the dynamic programming properties of uncertain Markov decision processes.
• Jan 26: one paper accepted at Operations Research, on Blackwell and average optimality in robust Markov decision processes. This is joint work with Nicolas Vieille (HEC) and Marek Petrik (UNH).
• Oct 25: I am a recipient of an ANR grant (2026-2028) on uncertainty set selection in sequential decision-making problems, joint with Nian Si at HKUST.
• Oct 25: one paper accepted at the NeurIPS 2025 DynaFront workshop, with the best paper award!
• Sep 25: one paper accepted at NeurIPS 2025 (poster), on the interplay between mean-payoff and discounted stochastic games. This is joint work with Stéphane Gaubert (CMAP, Ecole Polytechnique) and Ricardo Katz (CIFASIS-CONICET).
• Sep 25: I am now an associate professor at HEC!

Publications and preprints

Journals:

• Beyond discounted returns: Robust Markov decision processes with average and Blackwell optimality
  Julien Grand-Clément, Marek Petrik, Nicolas Vieille.
  Operations Research (accepted, 2026)
• On the convex formulations of robust Markov decision processes
  Julien Grand-Clément, Marek Petrik.
  Mathematics of Operations Research (2024), 50(3):1681-1706.
• The best decisions are not the best advice: Making adherence-aware recommendations
  Julien Grand-Clément, Jean Pauphilet.
  Management Science (2024), 72(1):667-692. The code for the simulations is available here.
• Solving optimization problems with Blackwell approachability
  Julien Grand-Clément, Christian Kroer.
  Mathematics of Operations Research (2023), 49(2):697-728. Slides are available here.
• Robustness of Proactive Intensive Care Units Transfer Policies
  Julien Grand-Clément, Carri Chan, Vineet Goyal, Gabriel Escobar.
  Operations Research (2022) 71(5):1653-1688. The code for the simulations is available here.
• Quantifying Utilitarian Outcomes to Inform Triage Ethics: Simulated Performance of a Ventilator Triage Protocol Under SARS-CoV-2 Pandemic Surge Conditions
  Elizabeth Chuang, Julien Grand-Clément, Jen-Ting Chen, Carri Chan, Vineet Goyal, Michelle Ng Gong.
  American Journal of Bioethics – Empirical Bioethics (2022), vol. 13, no 3, p. 196-204.
• A First-Order Approach to Accelerated Value Iteration
  Vineet Goyal, Julien Grand-Clément.
  Operations Research (2022), 71(2):517-535.
• Robust Markov Decision Process: Beyond Rectangularity
  Vineet Goyal, Julien Grand-Clément.
  Mathematics of Operations Research (2022), 48(1):203-226.
• The Operator Approach to Entropy Games
  Marianne Akian, Stéphane Gaubert, Julien Grand-Clément, Jérémie Guillaud.
  Theory of Computing Systems (2019) 63(5), 1089-1130. A preliminary version appeared in STACS 2017.

Refereed conferences:

• Thresholds for sensitive optimality and Blackwell optimality in stochastic games
  Stéphane Gaubert, Julien Grand-Clément, Ricardo D. Katz
  (NeurIPS 2025) 38th Advances in Neural Information Processing Systems.
• Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games
  Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng.
  (ICLR 2025)  International Conference on Learning Representations, 15th.
• Risk-averse Total-rewards MDPs with ERM and EVaR
  Xihong Su, Julien Grand-Clément, Marek Petrik.
  (AAAI 2025, oral) Proceedings of the AAAI Conference on Artificial Intelligence, 39th.
• Extensive-Form Game Solving Via Blackwell Approachability on Treeplexes
  Darshan Chakrabarti, Julien Grand-Clément, Christian Kroer.
  (NeurIPS 2024, spotlight) 38th Advances in Neural Information Processing Systems.
• Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms
  Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng.
  (NeurIPS 2024) 38th Advances in Neural Information Processing Systems. Some slides and the code for the experiments.
• Regret Matching+: (In)Stability and Fast Convergence in Games
  Gabriele Farina, Christian Kroer, Julien Grand-Clément, Chung-Wei Lee, Haipeng Luo.
  (NeurIPS 2023, spotlight) 37th Advances in Neural Information Processing Systems. The code for the simulations is available here. Some slides are available here.
• Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
  Julien Grand-Clément, Marek Petrik.
  (NeurIPS 2023) 37th Advances in Neural Information Processing Systems.
• Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving
  Julien Grand-Clément, Christian Kroer.
  (NeurIPS 2021) 34th Advances in Neural Information Processing Systems.
• First-Order Methods for Wasserstein Distributionally Robust MDP
  Julien Grand-Clément, Christian Kroer.
  (ICML 2021) 38th International Conference on Machine Learning.
• Scalable First-Order Methods for Robust MDPs
  Julien Grand-Clément, Christian Kroer.
  (AAAI 2021) Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 12086-12094.

Refereed workshop at conferences:

• On the Separation Between Best-Iterate, Random-Iterate, and Last-Iterate Convergence of Learning in Games
  Y. Cai, G. Farina, J. Grand-Clément, C. Kroer, C.-W. Lee, H. Luo, W. Zheng
  (NeurIPS 2025) 39th Advances in Neural Information Processing Systems 
  Workshop: Dynamics at the Frontiers of Optimization, Sampling, and Games
  (Best paper award for the workshop)
• Optimality of stationary policies in risk-averse total-reward MDPs with EVaR
  X. Su, M. Petrik, J. Grand-Clément
  (ICML 2024) 41st International Conference on Machine Learning 
  Workshop: Foundations of Reinforcement Learning and Control

Working papers and preprints:

• Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage
  Julien Grand-Clément, You-Hui Goh, Carri Chan, Elizabeth Chuang, Vineet Goyal.
  Preliminary version accepted at the American Thoracic Society 2021 International Conference.
• Tractable Markov Decision Processes
  Julien Grand-Clément, Nian Si, Shengbo Wang.
• On the Separation Between Best-Iterate, Random-Iterate and Last-Iterate Convergence of Learning in Games
  Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng.
• Playing against a stationary adversary
  Julien Grand-Clément, Nicolas Vieille
• Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes
  Axel Benyamine, Julien Grand-Clément, Marek Petrik, Michael I. Jordan, Alain Durmus

Technical report:

• From Convex Optimization to MDPs: A Review of First-Order, Second-Order and Quasi-Newton Methods for MDPs
  Julien Grand-Clément.

Teaching

• HEC Paris, 2021 - present:
  • Introduction to Data Science, graduate core course (~ 200 students)
  • Operations and Supply Chain Management, graduate core course (~ 300 students)
  • Sustainable Operations and Supply Chain Management, undergraduate core course (~ 140 students)
  • Sustainable Artificial Intelligence, undergraduate core course (~ 400 students)
  • Artificial Intelligence and Climate Change, certificate course (~ 50 students). Slides available here.
  • Les grands défis de l'IA, undergraduate (~ 50 students)

• Columbia University, 2016 - 2021, undergraduate & graduate courses (~ 450 students).
  • Convex Optimization (IEOR 6616).
  • Advanced Optimization (IEOR 4004).
  • Foundations of Optimization (IEOR 3608).
  • Dynamic Pricing & Revenue Management (IEOR 4601).
  • Game Theoretic Models of Operations (IEOR 4407).
  • Operations Management (IEOR 4000).