Silvia Ferrari is John Brancaccio Professor of Mechanical and Aerospace Engineering at Cornell University. Prior to that, she was Professor of Engineering and Computer Science at Duke University, and Founder and Director of the NSF Integrative Graduate Education and Research Traineeship (IGERT) and Fellowship program on Wireless Intelligent Sensor Networks (WISeNet). Currently, she is the Director of the Laboratory for Intelligent Systems and Controls (LISC) at Cornell University and the co-Director of the Cornell-Unibo Věho Institute on Vehicle Intelligence at Cornell Tech. Her principal research interests include active perception, robust adaptive control, learning, and approximate dynamic programming, and control of multiscale dynamical systems. She is the author of the book “Information-driven Path Planning and Control,” MIT Press (2020), and of the TED talk “Do robots dreams of electric sheep?”. She received the B.S. degree from Embry–Riddle Aeronautical University and the M.A. and Ph.D. degrees from Princeton University. She is a Fellow of ASME, a Senior Member of the IEEE and AIAA, and a Member of SPIE and SIAM. She is the recipient of the ONR young investigator award (2004), the NSF CAREER award (2005), and the Presidential Early Career Award for Scientists and Engineers (PECASE) award (2006).
Professor Ferrari's research focuses on design and analysis of methods and algorithms for computational intelligence and sensorimotor learning and control. Her contributions include the development of new theories and algorithms on the learning and approximation properties of graphical models, such as neural and probabilistic networks, as well their applications in many areas of science and engineering, such as reconfigurable aircraft control and robotics. Professor Ferrari developed new methods for adaptive dynamic programming, reinforcement learning, optimal control, and information-driven planning and control for distributed systems and mobile sensor networks. Recent contributions also include the development of new mathematical models of learning and plasticity uncovered from biological brains, as well as cognitive models of complex decision making derived from data.
Optimal control theory, intelligent systems, multivariable control, feedback control of dynamic systems, sensor networks.
