Yuanyuan Shi

Biography

I am an Assistant Professor in the Electrical and Computer Engineering Department at UCSD. I am affiliated with the Center for Energy Research and the Center for Machine Intelligence, Computing & Security. My research interests broadly lie in machine learning, dynamical systems, and control, with applications to power & energy systems and other intelligent systems.

I received my Ph.D. (2020) in Electrical and Computer Engineering and M.S. (2020) in Statistics from the University of Washington, Seattle. Prior to UCSD, I spent 1 year as a postdoc fellow in the Computing and Mathematical Sciences Department at Caltech, and several research internships in Google DeepMind, JD Research, and Doosan Gridtech.

News

2025/05: ICML 2025 Analytical Lyapunov Function Discovery via RL
2025/05: L4DC 2025 Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems (selected for Oral Presentation and Best Paper Award Nominee)
2025/04: We are excited to announce a Special Issue in Applied Energy on the topic of “Trustworthy Machine Learning for Power and Energy Systems”. This issue focuses on advancing reliable AI/ML solutions for critical energy infrastructure, addressing challenges like interpretability, robustness, and security. Submissions are open until November 30, 2025. Learn more and submit your work here
2025/03: Awarded CEC project on grid-supportive mobile charging solutions, see our Postdoc Opening!
2025/01: Checkout recent papers from our group:
- Applied Energy: PDE-based Building Airflow Modeling and Control
- ICLR 2025 Planning Neural Operator (PNO)
- IEEE Transactions on Power Systems: Stability-constrained RL for Frequency Control under Time-varying Inertia
2025/01: Honored to receive the AI2050 Early Career Fellowship from Schmidt Sciences!

More news

2024/12: Paper on value-oriented renewable energy forecasting to appear in IEEE Transactions on Smart Grid.
2024/09: Paper on Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control appears in IEEE Transactions on Control of Network Systems.
2024/09: Awarded DOE neural operator learning and scientific foundation model project. Check out our project website!
2024/07: New paper on PDE based models for building airflow modeling and control in Applied Energy.
2024/05: New paper paper on neural operator learning for PDE system control in IEEE Transactions on Automatic Control.
2024/01: Honored to receive the Jacobs School Early-Career Faculty Acceleration Award for our collaborative work with Prof. Jingbo Shang on building learning and control for health and sustainability.
2023/12: Our lab and collaborators presented two papers at NeurIPS 2023!
- Neural-PI Control with End-to-End Stability and Output Tracking Guarantees (We design a multi-input multi-output monotone neural network structure that guarantee stability of multi-agent RL in networked systems with passivity. The monotone neural network design can flexibly handle different communication structures: decentralized, distributed, and centralized!)
- SustainGym: Reinforcement Learning Environments for Sustainability Applications (A suite of Gym environments designed to test Multi-agent RL algorithms on sustainability tasks, including EVChargingEnv, ElectricityMarketEnv, DatacenterEnv, CogenEnv, BuildingEnv, with real-world data)
2023/12: Our lab and collaborators presented five papers at IEEE CDC 2023!
- Neural Operators for Hyperbolic PDE Backstepping Feedback Laws
- Neural Operators for Hyperbolic PDE Backstepping Kernels
- Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
- KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Discrete-Time Systems
- Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach With Safe Gradient Flow
2023/10: Our paper on "Optimal Vehicle Charging in Bilevel Power-Traffic Networks via Charging Demand Function" is accepted for publication in the IEEE Transactions on Smart Grid. The paper investigate the coordination of large-scale EV charging by considering both the transportation and power network physical constraints, via a novel design of EV charging demand function. Congrats Yufan!
2023/08: We released a new real-world multi-zone building dataset BEAR-Data (1 building, 80+ zones with detailed zonal temperature and 17 control commands data about 8 months) for smart building research. The accompanied paper is accepted to ACM BuildSys 2023.
2023/08: Our paper Predicting Strategic Energy Storage Behaviors is accepted for publication in the IEEE Transactions on Smart Grid. Congrats Yuexin!
2023/09: Invited speaker at the CDC 2023 Physics-informed Learning for Control and Optimization This workshop aims to provide insight into recent advances in the field of physics-informed machine learning for control and optimization. Please consider joining the workshop on December 12th, 2023 @CDC 2023, Singapore.
2023/09: Invited speaker at the Sixth AES Workshop organized by the National Renewable Energy Laboratory (NREL). I presented our recent line of work on stability-constrained RL for voltage control. Check out the presentation slides here.
2023/07: Our paper on Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach With Safe Gradient Flow is accepted for publication in the IEEE Control Systems Letters and will also be presented at IEEE CDC 2023. Congrats Jie!
2023/07: Yufan presented our work on Combining Data and Physics Knowledge for Demand Response Forecast in Energy Systems at the 2023 IEEE Power and Energy System General Meeting!
2023/06: Our paper on Operator Learning for Nonlinear Adaptive Control is accepted and presented at the L4DC 2023. Congrats Luke! Check out our project website.
2023/05: Invited speaker at the ACC 2023 Workshop on Physics-Informed System Identification
2023/05: Invited speaker at the AFOSR Workshop on Intersection of Deep Learning and Computational Nonlinear Control
2023/05: Honored to receive the Hellman Fellowship
2023/04: Invited speaker at the UCSD Control Systems & Dynamics Seminar.
2023/02: Invited speaker at ITA 2023 session in machine learning and control.
2023/01: Honored to receive the Jacobs School Early Career Faculty Development Award for our collaborative work with Prof. Patricia Hidalgo-Gonzalez on reinforcement learning for microgrid control
I co-organized the Control Meets Learning online seminar series in 2020 - 2021. This repo contains many interesting talks on the intersection of control and learning and future outlooks. Please check out this website for the recordings.
2022/10: Invited talks at Allerton 2022 and INFORMS 2022 about "Stability Constrained Reinforcement Learning for Real-Time Voltage Control" Check out our slides here
2022/10: Invited talk at the Pacific Northwest National Laboratory about Learning and Control for Sustainable Energy Systems.
2022/06: Our work on "Demand Response Model Identification and Behavior Forecast with OptNet: a Gradient-based Approach" at the ACM International Conference on Future Energy Systems (ACM e-Energy) conference.
2022/06: Our paper Robust online voltage control with an unknown grid topology is presented at the ACM International Conference on Future Energy Systems (ACM e-Energy) and selected as Best Paper Finalist.

Teaching

ECE 171B: Linear Control System Theory: [2024 Fall][2023 Fall] [2021 Fall] [2022 Fall]
ECE 228 Machine Learning for Physical Applications: [2025 Spring] [2024 Spring] [2023 Spring] [2022 Spring]

Prospective Students

Postdocs Opening

Check out Postdoctoral Opening for currently available positions.

PhD Applicants

I am always looking for highly motivated and self-driven Ph.D. with a strong mathematical background and interested in machine learning, control, and power/energy systems. Please apply through the UCSD ECE PhD program (ISRC and MLDS tracks) (Recent Application Deadline: 2024/12/16). Apologies for not being able to answer your inquiry emails about the application. There are also positions for master/undergraduate research. Please checkout the Join us page for more information.

Contact

Email: yyshi@ucsd.edu

Office: Franklin Antonio Hall