John Brancaccio Professor of Mechanical and Aerospace Engineering
Silvia Ferrari, Professor with indefinite tenure, Sibley School of Mechanical and Aerospace Engineering, College of Engineering, was elected the first John Brancaccio Professor of Mechanical and Aerospace Engineering, effective November 1,2018.
Biography
Silvia Ferrari is a Professor of MAE at Cornell University. Prior to that, she was Professor of Engineering and Computer Science, and Founder and Director of the NSF Integrative Graduate Education and Research Traineeship (IGERT) and Fellowship program on Wireless Intelligent Sensor Networks (WISeNet) at Duke University. She is the Director of the Laboratory for Intelligent Systems and Controls (LISC), and her principal research interests include robust adaptive control of aircraft, learning and approximate dynamic programming, and optimal control of mobile sensor networks. 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 senior member of the IEEE, and a member of ASME, SPIE, and AIAA. 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).
Research Interests
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.
Education
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