Welcome to the Control and Optimization Research (CORE) Laboratory.
Our research focuses on control and optimization methods, risk management solutions and incentive mechanisms for IoT-enabled systems and networked CPS with limited information.
Assistant Professor of Electrical Engineering
University of Southern California (USC)
EEB 316, 3740 McClintock Ave.
Los Angeles, CA 90089-2563, USA
- (Aug. 2017) Insoon received a grant from NSF for our project, "Distributionally Robust Control and Incentives with Safety and Risk Constraints".
- (Jul. 2017) Our paper "A dynamic game approach to distributionally robust safety specifications for stochastic systems" has been provisionally accepted to Automatica.
- (Jul. 2017) Our paper "A convex optimization approach to distributionally robust Markov decision processes with Wasserstein distance" has been published in the IEEE Control Systems Letters.
- (Apr. 2017) Our paper "Variance-constrained risk sharing in stochastic systems" (co-authored by Duncan Callaway and Claire Tomlin) has been published in the IEEE Trans. Automatic Control.
- (Mar. 2017) Our paper "Optimal control of conditional value-at-risk in continuous time" (co-authored by Christopher W. Miller) has been published in the SIAM J. on Control and Optimization.
- (Jan. 2017) Insoon received a grant from NSF for our project, "CRII: CPS: Information-Constrained Cyber-Physical Systems for Supermarket Refrigerator and Inventory Management".
- (Oct. 2016) Our paper "Submodularity of energy storage placement in power networks" (co-authored by Junjie Qin and Ram Rajagopal) has been selected as a finalist for the Best Student Paper Award at the IEEE CDC 2016.
- (Sep. 2016) Our paper "Approximation algorithms for optimization of combinatorial dynamical systems" (co-authored by Sam Burden, Ram Rajagopal, Shankar Sastry and Claire Tomlin) has been published in the IEEE Trans. Automatic Control.
- (Aug. 2016) The Control and Optimization Research (CORE) lab has been launched.
- Theory: Control, Optimization
- Distributionally robust stochastic control and reinforcement learning
- Risk- & safety-aware optimal control
- Stochastic, nonconvex optimization
- Optimization of dynamical networks
- Incentives in systems and control
- Applications: Cyber-Physical Systems, IoT
- Energy systems: commerical/residential energy management systems, energy storage, electricity markets, energy policy