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 sustainable 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. 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
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. See our research on operator learning for PDE control.
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: Awarded 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)
More news
2023/12: Our lab and collaborators presented five papers at IEEE CDC 2023!
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 [2024 Spring] [2023 Spring] [2022 Spring]
Prospective Students
I am always looking for highly motivated and self-driven Ph.D. with a strong mathematical background and interested in machine learning and control. Please apply through the UCSD ECE PhD program (ISRC and MLDS tracks) (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. If you are currently at UCSD, please fill out the Application Form first and send me an email with your resume and UCSD transcript once you filled out the application. For all other applicants: If you are interested in visiting opportunities, please fill out Visiting Scholar Form and send me an email.
Contact
Email: yyshi@ucsd.edu
Office: Franklin Antonio Hall
Copyright © Yuanyuan Shi. All rights reserved.