Lili Wang is a Lillian Gilbreth Postdoctoral Fellowship postdoc at Purdue University. She received the Ph.D. degree in Electrical Engineering from Yale University in 2020 under the supervision of Prof. A. Stephen Morse where she did research in the area of distributed computation and estimation. Prior to that, she was a Postdoctoral Associate at University of California, Irvine. She received the B.S. and M.S. degrees in Control from Zhejiang University, China where she worked on multi-agent systems.
Over the past few years there have been a number of advances in the distributed algorithms for multi-agent system. My research falls into the following three categories: estimation, control and learning. The distributed state estimation problem is to develop estimators, one for each agent, to estimate the state of a continuous-time, jointly observable system whose sensed outputs are distributed across a network. The distributed control problem is how to achieve a formation shape with only local relative information available. The distributed learning problem becomes juicy to deal with large data problems, including distributed clustering, deep learning and so on. These findings suggest that other centralized control topics such as non-interacting control, adaptive control, and linear optimal control may be amenable to distributed generalizations, which will be one direction for future work.