Cornell engineers are experimenting with new programming that mimics the way an insect’s brain works in order to power tiny robots. Read more about Engineers program tiny robots to move, think like insects
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).
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.
- Statistics and Machine Learning
- Artificial Intelligence
- Complex Systems, Network Science and Computation
- Atmospheric Science and Climate
- Information Theory and Communications
- Nonlinear Dynamics
- Remote Sensing
- Sensors and Actuators
- Signal and Image Processing
- Systems and Networking
- Theory of Computation
Optimal control theory, intelligent systems, multivariable control, feedback control of dynamic systems, sensor networks.
- 2017."A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks."International Journal of Neural Systems 1750015. .
- 2016."Information Value in Nonparametric Dirichlet Process Gaussian Process (DPGP) Mixture Models of Target Kinematics."Automatica74: 360-368. .
- 2016."A Model-based Approach to Optimizing Ms. Pac-Man Game Strategies in Real Time."IEEE Transactions on Computational Intelligence and AI in Games 1-1. .
- 2016."Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking."IEEE Transactions on Control of Network Systems 1-1. .
- 2016."Proc. of the IEEE Conference on Decision and Control, Las Vegas, NV."Paper presented at Value Function Approximation for the Control of Multiscale Dynamical Systems, December. .
Selected Awards and Honors
- Presidential Early Career Award for Scientists and Engineers (PECASE) 2006
- International Crime Analysis Association Research Award 2005
- National Science Foundation CAREER Award (NSF) 2005
- Office of Naval Research Young Investigator Award 2004
- NC Space Grant Consortium Research Seed Award 2003
- B.S. (Aerospace Engineering), Embry-Riddle Aeronautical University, 1997
- M.A. (Mechanical and Aerospace Engineering), Princeton University, 1999
- Ph.D. (Mechanical and Aerospace Engineering), Princeton University, 2002
In the News
Researchers are developing a system to enable teams of robots to share information as they move around and if necessary get help in interpreting what they see, enabling them to conduct surveillance as a single entity with many eyes. Read more about Researchers link robots to surveillance teams