Wenqi Cui

Wenqi Cui

Postdoctoral Scholar

California Institute of Technology

I am currently a postdoctoral scholar in the department of Computing + Mathematical Sciences at California Institute of Technology (Caltech), advised by Prof. Steven Low and Prof. Adam Wierman. I am honored to be a recipient of the Pioneer Postdoctoral Fellowship and PIMCO Postdoctoral Fellowship at Caltech. I obtained my Ph.D. in Electrical and Computer Engineering from the University of Washington, advised by Prof. Baosen Zhang.

My research interests lie broadly in control, machine learning and optimization for cyber-physical energy systems. Recently, I am developing structured control and learning methods with provably guarantees on safety-critical constraints and time-varying adaptations. I’m also working on efficient algorithums to overcome the challenges on sample complexity and explorations in learning for real-world applications (e.g., power systems).

Openings: I am excited to be joining the Department of Electrical and Computer Engineering at New York University (NYU) as a tenure-track Assistant Professor in September 2025. I am looking for self-motivated PhD students and postdoc starting in Fall 2025. If you are interested, feel free to email me (wenqicui@caltech.edu) with your CV and transcripts.

Download my CV.

Interests
  • Control and Optimization
  • Machine Learning
  • Cyber-Physical Energy Systems
Education
  • PhD in Electrical and Computer Engineering, 2019-2024

    University of Washington

  • MS in Electrical Engineering, 2016-2019

    Zhejiang University

  • BEng in Electrical Engineering and Automation, 2012-2016

    Southeast University

Recent News

All news»

[2024/06] Disertation defense and ECE graduation in one day πŸŽ‰. Heartfelt thanks to my advisor, PhD committee, family, friends, and everyone who has helped and supported me throughout this wonderful PhD journey πŸ€—.

[2023/12] I will present our work Leveraging Predictions in Power System Frequency Control: an Adaptive Approach at IEEE CDC 2023 in the session WeA16 Computational Techniques for Automation in Energy Systems. Welcome to drop by our talks.

[2023/12] I will present my research centers around structured control and learning for energy systems in the Meet the Faculty Candidates Poster Session at IEEE CDC 2023. Welcome to drop by our posters.

[2023/09] Our paper on Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees is accepted in NeurIPS 2023.

[2023/06] Our paper on Leveraging Predictions in Power System Frequency Control: an Adaptive Approach and Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach With Safe Gradient Flow are accepted in IEEE CDC 2023.

Recent Publications

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(2023). Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees. NeurIPS 2023.

PDF Code Project

(2023). Reinforcement Learning for Optimal Frequency Control: A Lyapunov Approach. IEEE Transactions on Power Systems.

PDF Cite Code Project

(2023). Efficient Reinforcement Learning Through Trajectory Generation. Learning for Dynamics and Control Conference.

PDF Code Project

(2022). Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach. IEEE Open Journal of Control Systems.

PDF Project

Projects

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Biography

 
 
 
 
 
Ph.D. Student in Electrical and Computer Engineering
University of Washington
Ph.D. Student in Electrical and Computer Engineering
Sep 2019 – Present Seattle, WA

Research experience include:

  • Safe reinforcement learning with Lyapunov staility
  • Optimal frequency control and voltage control in power systems
  • Power system dynamic prediction under large disturbances
  • Inter-disciplinary research in Clearn Energy Institute, including traffic prediction and few-shot learning for Electron Microscopy Data
 
 
 
 
 
M.S. in Electrical Engineering
Zhejiang University
M.S. in Electrical Engineering
Sep 2016 – Jun 2019 Hangzhou, P.R. China

Research experience include:

  • Optimal dispatch of demand response resources in power systems
  • Reliability evaluation of power systems with distributed energy resources
  • Participate in projects to implement demand response programs in Jiangsu and Zhejiang Province
 
 
 
 
 
BEng in Electrical Engineering and Automation
Southeast University
BEng in Electrical Engineering and Automation
Sep 2012 – Jun 2016 Nanjing, P.R. China

Research experience include:

  • Optimal planning of microgrid
  • Power system black-start with distributed energy resources

Internship

 
 
 
 
 
Research Intern
Microsoft Research
Research Intern
Jun 2022 – Sep 2022 Redmond, WA
I interned at Microsoft Research Special Projects at Remond Lab, mentored by Weiwei Yang. We proposed sample-efficient reinforcement learning algorithms for the control of largescale physical systems, including power systems and traffic networks. The proposed methods overcome the challenges of partial observability, sample complexity and the lack of real-time communication capability in real-world applications.
 
 
 
 
 
Research Intern
Microsoft Research
Research Intern
Jun 2021 – Sep 2021 Redmond, WA
I interned at Microsoft Research Special Projects at Remond Lab, mentored by Weiwei Yang. We worked on AI for sustainable energy systems. We proposed a novel framework for power system dynamic predictions by learning in the frequency domain. Compared with state-of-the art AI methods including Physics-Informed neural networks and generic deep neural network, the proposed method reduce MSE prediction error by more than 50% and vastly improves the detection of unstable dynamics. It also provides a computation speed up of more than 400 times compared to existing power system tools.

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