Meiyi Li

Meiyi Li

Assistant Professor at LSU. Agentic AI, machine learning, optimization, and trustworthy decision-making for cyber-physical infrastructure.

Meiyi Li

I am Meiyi Li, a tenure-track Assistant Professor in the Division of Electrical & Computer Engineering at Louisiana State University (LSU).

I received my Ph.D. in Civil, Architectural, and Environmental Engineering from The University of Texas at Austin in August 2026, advised by Prof. Javad Mohammadi.

I am recruiting Ph.D. students to join my group, the OPAL Lab (Optimization · Power Systems · Agentic AI · Learning), in Spring 2027 or Fall 2027. Prospective students interested in power and energy systems, agentic AI, machine learning, optimization, and intelligent cyber-physical infrastructure are encouraged to read the recruiting page and submit the online interest form.

OPAL Lab

Join the OPAL Lab

My research develops agentic AI, machine learning, and optimization methods for decision-making under uncertainty in power and energy systems and other complex cyber-physical infrastructure. I am especially interested in building intelligent, trustworthy, and sustainable decision-making systems that can operate reliably in real-world power grids, energy systems, and infrastructure networks.

I am honored to have collaborated with Prof. Soheil Kolouri, Prof. Kyri Baker, and Prof. Constance Crozier on projects including trustworthy agentic AI, distributed optimization, and the ARPA-E Grid Optimization Competition.

Before joining UT Austin, I was a Ph.D. student in Electrical and Computer Engineering at Carnegie Mellon University, where I worked with Prof. Soummya Kar and Prof. Javad Mohammadi on distributed optimization for energy markets.

I earned both my B.S. and M.S. in Electrical Engineering from Shanghai Jiao Tong University, as part of the Outstanding Engineers Honor Class, under the supervision of Prof. Nengling Tai and Prof. Wentao Huang.

Outside of research, I enjoy hiking and swimming.

Hiking and swimming

Education

Research Interests

Current research directions include:

  • Optimization and learning for power, energy, mobility, smart city systems, and data centers
  • Agentic AI and machine learning for decision-making under uncertainty
  • LLM agents and multi-agent systems for cyber-physical infrastructure
  • Trustworthy, robust, and risk-aware agentic AI
  • Sustainable and carbon-aware intelligent systems

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