Biography
I am an Assistant Professor in the Electrical and Computer Engineering Department at UCSD. I am also affiliated with the Center for Machine Intelligence, Computing & Security and the Center for Energy Research. My research interests broadly lie in machine learning, dynamical systems and control, and sustainability. My lab focuses on various aspects of creating intelligent systems, with an emphasis on principled learning and control algorithms for sustainable energy and power systems.
Before joining UCSD, I was a postdoc fellow in the Computing and Mathematical Sciences Department at Caltech from 2020-2021, working with Adam Wierman and Anima Anandkumar. I obtained my Ph.D. from the Electrical and Computer Engineering Department at the University of Washington, advised by Baosen Zhang.
Teaching
ECE 171B: Linear Control System Theory [2021 Fall] [2022 Fall]
ECE 228 Machine Learning for Physical Applications [2022 Spring] [2023 Spring]
Recent talks and Collaborators Talks
Demand Response Model Identification and Behavior Forecast with OptNet
Robust Online Voltage Control with Mistake Guarantee
GMT20230414-220259_Recording_1920x1050.mp4Learning and Control for Energy Systems @UCSD Control Systems & Dynamics Seminar.
News
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 for our project on smart building control
2023/04: 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
2023/04: Presented "Learning and Control for Energy Systems" at the UCSD Control Systems & Dynamics Seminar.
2023/03: Paper on bridging transient and steady-state performance in voltage control: A reinforcement learning approach with safe gradient flow.
2023/03: Papers on Operator Learning for accelerating PDE control [ref1] [ref2]
2023/02: Invited speaker at ITA 2023 session in machine learning and control.
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.
I co-organized Control Meets Learning in 2020 - 2021, a virtual seminar series that contained many interesting talks on the intersection of control and learning. Check out our website for the recordings.
Prospective Students and Postdocs
We are looking for highly motivated and self-driven Ph.D. students and postdoctoral candidates with a strong mathematical background and foundation in machine learning, control, and energy systems. 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.
Both theoretical and empirical research is carried out in the group and students who can build bridges between the two, and also between different disciplines will be a good fit here.
Contact
Email: yyshi@eng.ucsd.edu
Office: Franklin Antonio Hall 2406
Copyright © Yuanyuan Shi. All rights reserved.