Patrick Jaillet

Massachusetts Institute of Technology, Moscow Institute of Thermal Technology

Papers

Total Citations

142

H-Index

About

Patrick Jaillet is a leading figure in the fields of operations research, artificial intelligence, and robotics, with a particular focus on decision-making under uncertainty and large-scale optimization. His work bridges the gap between theoretical foundations and high-impact applications, most notably in the development of intelligent transportation systems. Jaillet’s major contributions include pioneering decentralized data fusion and active sensing methods for spatiotemporal modeling in Mobility-on-Demand (MoD) systems, a paper that has garnered 106 citations and helped shape the future of autonomous vehicle fleets. He has also advanced the theory of decentralized stochastic planning, addressing the critical challenge of anonymous interactions among agents, and introduced novel risk-aware optimization algorithms, such as the Value-at-Risk Upper Confidence Bound (V-UCB) method, which provides the first no-regret guarantee for optimizing black-box functions under risk constraints. His work is characterized by its rigorous mathematical depth and its direct relevance to real-world systems, from robotic mobility to financial risk management. Jaillet’s research has profoundly influenced how autonomous systems perceive, plan, and operate in complex, uncertain environments.