Nikolai Matni

Nikolai Matni is an Associate Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research lies at the intersection of control theory, optimization, and machine learning, with a focus on designing and analyzing safe, data-driven, and scalable algorithms for cyber-physical and autonomous systems. Matni investigates how learning can be rigorously integrated with feedback control to improve the performance and robustness of systems operating under uncertainty, while ensuring safety and stability guarantees. His work spans topics such as robust and optimal control, learning-based motion planning, perceptual control loops, neural ODE modeling, uncertainty-aware navigation, and fundamental performance limits of learned controllers. Prior to joining Penn, Matni was a postdoctoral scholar in EECS at UC Berkeley and in Computing & Mathematical Sciences at Caltech. He received his Ph.D. in Control and Dynamical Systems from Caltech, and holds B.A.Sc. and M.A.Sc. degrees in Electrical Engineering from the University of British Columbia. His long-term vision is to build principled theoretical foundations for intelligent, safe, and reliable autonomous systems deployed in complex real-world environments.