Chen's research aims to build rigorous foundations and developing new methodologies in optimization and systems theory for distributed control and learning of networked dynamical systems. These systems are large-scale with interconnected, active, and possibly self-interested components, operate with incomplete information and in uncertain environments, and must achieve certain desired network-wide objectives or collective behaviors. Problems associated with such systems are typically large, computationally hard, and require distributed solutions; yet they are also very structured and have features that can be exploited by appropriate computational methods. Chen's research focuses on developing optimization approaches for such problems, and brings together optimization, systems theory, and domain-specific knowledges for exploring structures of the underlying problems and systems and leveraging them for the principled design of distributed control and learning architecture.
Control and optimization of networked systems, Cyber-physical networks and autonomous systems, Machine learning and its integration with control, Quantum Computing, Control and Optimization of Quantum Systems, Communication and computer networks, Distributed optimization and control, Convex relaxation and parsimonious solutions, Game theory and its engineering applications, Theoretical foundation of complex engineering networks
CSCI 3104 - Algorithms
Fall 2018 / Spring 2020 / Spring 2022
Covers the fundamentals of algorithms and various algorithmic strategies, including time and space complexity, sorting algorithms, recurrence relations, divide and conquer algorithms, greedy algorithms, dynamic programming, linear programming, graph algorithms, problems in P and NP, and approximation algorithms. Same as CSPB 3104.
CSCI 5254 - Convex Optimization and Its Applications
Spring 2018 / Spring 2019 / Fall 2020 / Fall 2021
Discuss basic convex analysis (convex sets, functions and optimization problems), optimization theory (linear, quadratic, semidefinite and geometric programming; optimality conditions and duality theory), some optimization algorithms (descent methods and interior-point methods), basic applications (in signal processing, control, communications, networks, statistics, machine learning, circuit design and mechanical engineering, etc.), and some advanced topics (distributed decomposition, exact convex relaxation, parsimonious recovery).