Stephanie Gil

About

Stephanie Gil is an Associate Professor of Computer Science at Harvard University and the John A. Paulson School of Engineering and Applied Sciences, and a Kempner Institute Associate Faculty member. Her research lies at the intersection of robotics, networked systems, and learning, with a focus on enabling resilient and intelligent multi-agent systems in uncertain, adversarial, or dynamic environments. She leads the Robotics, Embedded Autonomy and Communication Theory (REACT) Lab and works on applications ranging from multi-robot systems to cyber-physical system security. Gil earned her Ph.D. from the Massachusetts Institute of Technology (MIT). She is a recipient of the NSF CAREER Award, a Sloan Research Fellow, and has been recognized for her contributions to distributed autonomy and secure coordination in robotic networks.

Research Focus

Stephanie Gil’s research advances foundational understanding of natural and artificial intelligence by developing theory and systems for intelligent multi-agent coordination in uncertain, unstructured, and adversarial environments. She designs principled frameworks that enable physically embodied agents—such as robots—to reason, perceive, communicate, and learn in secure and coordinated ways. Her work explores how distributed agents can construct situational awareness, form trust, and make decisions in dynamic, partially observable settings. This includes developing resilient algorithms for consensus, control, and learning that adapt to uncertainty and adversarial behavior, as well as integrating reinforcement learning with trust modeling for real-time decision-making. These systems are deployed on physical platforms, grounding theoretical insights in real-world environments. By bridging robotics, machine learning, communication theory, and decision making, her research contributes to the Kempner Institute’s mission of understanding intelligence across natural and engineered systems, offering models of how distributed agents extract structure and meaning from sparse, noisy, or indirect observations.