Short Bio

I am a doctoral student in the group of Prof. Jalal Etesami. My specific research objectives involve bridging the gap between operations research and machine learning, and finding novel applications in the various areas of applied multi-agent systems.​​​​ Here is my biographical website.

My research interests include:

• Methods:
  • Stochastic Decision Processes
  • Reinforcement Learning
  • Game Theory
• Applications:
  • Operations Management
  • Control and Planning
  • Multi-Agent Systems

Teaching & Supervision: If my research area excites you, feel free to reach out to discuss potential avenues of collaboration or M.Sc./B.Sc. supervision.

Key Papers

• L. Liu, K. Rasul, Y. Chao, J. Etesami. Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations. 40th AAAI Conference on Artificial Intelligence (AAAI) - Main Technical Track. arXiv:2502.05498. 2026.
• L. Liu, J Etesami. Online Mixture of Experts: No-Regret Learning for Optimal Collective Decision-Making. 39th Conference on Neural Information Processing Systems (NeuRIPS) - Main Technical Track. arXiv:2502.05498. 2025.
• L. Liu, S. Liu, M. Jusup. Optimizing Stochastic Control through State Transition Separability and Resource-Utility Exchange. ACM SIGMETRICS Performance Evaluation Review. Vol. 52, No. 2. DOI: 10.1145/3695411.3695423. 2024.
• L. Liu, Y. Rong. No-Regret Learning for Stackelberg Equilibrium Computation in Newsvendor Pricing Games. 8th International Conference on Algorithmic Decision Theory (ADT). arXiv:2404.00203. 2024.

All Publications: Google Scholar/ DBLP

Public Service

• Program Committee for Conferences: AAAI 2025, DAI 2025
• Reviewer for Conferences: ICLR 2024-2026, NeuRIPS 2024-2025, ICML 2025, AISTATS 2024-2025, UAI 2024-2025
• Reviewer for Journals: INFORMS Journal on Computing, International Journal of Production Economics