Research

My research lies at the intersection of Control, AI, and Energy Systems. The overarching goal is to develop learning-enabled decision-making methods that are not only effective from data, but also interpretable, reliable, and grounded in system structure.

Topics: Data-Driven Control + Koopman Operators · Control Theory + LLMs · Machine Learning + Sustainable Energy Systems

Data-Driven Control + Koopman Operators

Modern systems are often too complex for accurate first-principles modeling, yet purely black-box models can be difficult to use for control. We study structured, data-driven representations of nonlinear dynamics, particularly Koopman-based and bilinear models, that connect learning from trajectories with predictive and optimal control.

Questions of Interest

How can we learn control-oriented representations of nonlinear systems from limited trajectory data?
How can Koopman and bilinear structure be translated into tractable predictive and optimal controllers?
What guarantees can be established for data-driven models operating in closed loop?

Related Works

[CDC'25]	Data-enabled predictive control for nonlinear systems based on a Koopman bilinear realization. [IEEE Xplore, arXiv]
[LCSS'22]	Data-driven optimal control of bilinear systems. [IEEE Xplore, arXiv]

Control Theory + Large Language Models

Large language and multimodal models increasingly operate as agents: they receive feedback, update decisions, and interact repeatedly with users and environments. We explore how ideas from feedback control, dynamical systems, and optimization can help understand and improve these closed-loop AI systems.

Current Directions

Feedback-based methods for aligning and adapting language and vision-language models.
Control-theoretic perspectives on reliability, robustness, and safety in iterative AI decision-making.
Learning and optimization methods for agents interacting with dynamic environments.

Selected Presentations

This is an active and growing research direction in the COLAS Lab. Related presentations will be added as the work develops; we welcome conversations with researchers working across control, machine learning, and foundation models.

Machine Learning + Sustainable Energy Systems

The transition to low-carbon energy brings new uncertainty, decentralization, and operational complexity. We develop learning-based controllers for power and energy systems while explicitly accounting for stability, safety, communication constraints, and equitable access to network resources.

Questions of Interest

How can learned controllers retain stability and safety guarantees when deployed on physical grids?
How should distributed energy resources coordinate under limited or changing communication?
How can efficiency, resilience, and equity be incorporated into data-driven energy management?

Related Works

[TSG'26]	Stability constrained voltage control in distribution grids with arbitrary communication infrastructures. [IEEE Xplore, arXiv]
[PSCC'24]	Unsupervised learning for equitable DER control. [ScienceDirect, arXiv]
[LCSS'23]	Learning decentralized frequency controllers for energy storage systems. [IEEE Xplore]
[LCSS'23]	Constraints on OPF surrogates for learning stable local Volt/Var controllers. [IEEE Xplore, arXiv]
[TPS'24]	Learning provably stable local Volt/Var controllers for efficient network operation. [IEEE Xplore, arXiv]
[SCL'24]	Reinforcement learning for distributed transient frequency control with stability and safety guarantees. [ScienceDirect, arXiv]

Related Presentations

2026	Data-Driven Control and Applications to Complex Energy Systems Chinese Symposium on Machine Learning and Scientific Applications (CSML), Aug. 2026.
2026	Learning Provably Stable Voltage Controllers Green Control Workshop, Peking University, May 2026.
2025	Data-Driven Learning and Control with Formal Guarantees Invited seminar series at SJTU, NYU Abu Dhabi, HKUST Guangzhou, CUHK Shenzhen, TU Delft, Oct. 2025–Jan. 2026.
2025	Data-Driven Learning Meets Control Theory: Voltage Control in Distribution Networks School of Data Science, CUHK Shenzhen, Jul. 2025.
2024	Learning for Control with Performance Guarantees: Applications to Power Networks Automatic Control Laboratory, ETH Zürich, Dec. 2024.
2024	Stability Constrained Voltage Control in Distribution Grids Department of Automation, SJTU, Jul. 2024.
2022	Safe Learning for Control in Power Networks National Academic Forum on Swarm Intelligent Unmanned Systems, ZJU, Dec. 2022.

Slides coming soon ...

See Publications for a complete list of papers. Prospective students and collaborators are also welcome to learn more about the COLAS Lab.