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Stefano Di Cairano is a leading researcher in autonomous systems, with key contributions spanning motion planning, decision-making, and control for autonomous vehicles, spacecraft, and legged robots. His work is distinguished by its focus on real-time, safety-guaranteed algorithms that bridge theory and practical deployment. Notably, his 2019 paper on "Motion Planning of Autonomous Road Vehicles by Particle Filtering" (52 citations) introduced a probabilistic method that leverages road network constraints for real-time decision-making, while his work on "Positive Invariant Sets for Safe Integrated Vehicle Motion Planning and Control" (37 citations) pioneered the use of feedback control and invariant sets to ensure collision-free trajectories with formal guarantees. Di Cairano has also advanced spacecraft autonomy through nonlinear model predictive control for rotational-translational rendezvous maneuvers, and extended his expertise to legged locomotion with auto-tuning controllers. His impact is evidenced by over 150 citations across his most-cited works, reflecting his role in shaping safe, autonomous navigation. A Senior Principal Scientist at Mitsubishi Electric Research Laboratories (MERL), Di Cairano's research consistently integrates rigorous control theory with machine learning, as seen in his imitation learning approach for multi-robot motion planning, making him a pivotal figure in the field of autonomous systems.