Research Overview

We study dynamical systems, complex networks,
and build ML tools that facilitate dynamic data analysis and learning.

Selected Previous Works


Event-driven learning and control for cyber-physical systems
Inferring dynamics of single neuron from in vitro recordings
Data-driven ensemble control systems
Distributed optimization: Parallel residual projection
Reservoir networks: Interpretable design
Nested two-stage clustering algorithm for dietary pattern analysis
Game-theoretic approaches to learning-based control synthesis
Reinforcement learning for model learning and knowledge sharing