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.