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.
keywords
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
Bloch wave oscillation and Coulomb blockade in mesoscopic electric circuits.
Physics Letters A: General Physics, Nonlinear Science, Statistical Physics, Atomic, Molecular and Cluster Physics, Plasma and Fluid Physics, Condensed Matter, Cross-disciplinary Physics, Biological Physics, Nanosciences, Quantum Physics.
446-450.
1998
Teaching
courses taught
CSCI 3104 - Algorithms
Primary Instructor
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Fall 2018 / Spring 2020 / Spring 2022 / Fall 2024
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 4831 - Special Topics in Algorithms
Primary Instructor
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Spring 2024
Covers topics of interest in computer science at the upper-division undergraduate level. Content varies from semester to semester. Only 9 credit hours from CSCI 4830 and/or CSCI 4831 can count toward Computer Science BS or BA.
CSCI 5254 - Convex Optimization and Its Applications
Primary Instructor
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Spring 2018 / Spring 2019 / Fall 2020 / Fall 2021 / Fall 2023 / Fall 2024
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).
CSCI 6950 - Master's Thesis
Primary Instructor
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Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022 / Summer 2022
CSCI 7000 - Current Topics in Computer Science
Primary Instructor
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Fall 2021 / Fall 2023 / Spring 2024 / Fall 2024
Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 18 total credit hours.
