Lydia E. Kavraki

Dr. Kavraki’s research spans robotics, computational biomedicine, and physical AI. In robotics, she focuses on enabling robots to collaborate effectively with and in support of people by developing the underlying computational methodologies that make such interaction possible. Her work advances algorithms for motion planning, novel approaches to reasoning under uncertainty, integrated frameworks for long-horizon planning, methods for learning from experience, and high-level instruction and collaboration paradigms. Her contributions to sampling-based motion planning are credited with reducing planning times from minutes to microseconds on conventional processors. In computational biomedicine, Dr. Kavraki applies machine learning and robotics-inspired engineering approaches to design methods and tools for modeling protein structure and function, elucidating biomolecular interactions, accelerating drug discovery, and integrating biological and biomedical data to improve human health. Her work targets improved protein function annotation and personalized immunotherapy. Uniting algorithms, statistical reasoning, formal methods, machine learning, data science, and physics-based modeling, Dr. Kavraki seeks to advance physical AI by enabling computers to reason robustly about complex real-world problems and contribute to transformative solutions in science, medicine, and engineering.