Yuanyuan Shi
Assistant Professor
Develops learning-based control with stability guarantees, including reinforcement learning and neural operators for energy and power systems.
Yuanyuan Shi's lab at UCSD works on reinforcement learning and manipulation & robot learning. 28 papers since 2023. 5 PhD students and 2 master's students. 1 alumnus. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 0%
- PhD students
- 5
- MS students
- 2
- alumni listed
- 1
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 2 papers2025 · T1 0
- 12 papers2024 · T1 0
- 14 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Reinforcement learning12 papers
- Manipulation & robot learning12 papers
- operators4 papers
- neural operators4 papers
- develops learning-based0 papers
Paper list
From the lab homepage.
20252 papers
202412 papers
- —Contributions of Individual Generators to Nodal Carbon Emissions (opens in a new tab)
- —Moving-Horizon Estimators for Hyperbolic and Parabolic PDEs in 1-D (opens in a new tab)
- —Neural operators for bypassing gain and control computations in PDE backstepping (opens in a new tab)
- —Neural operators of backstepping controller and observer gain functions for reaction-diffusion PDEs (opens in a new tab)
- —Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation (opens in a new tab)
- —Online Learning for Robust Voltage Control under Uncertain Grid Topology (opens in a new tab)
- —PDE Control Gym: A Gym for Data-driven Control and Reinforcement Learning of Partial Differential Equations (opens in a new tab)
- —Risk-Aware Value-Oriented Net Demand Forecasting for Virtual Power Plants (opens in a new tab)
- —Stability Constrained Reinforcement Learning for Real-Time Voltage Control in Distribution Systems (opens in a new tab)
- —Stability-Constrained Learning for Frequency Regulation in Power Grids with Variable Inertia (opens in a new tab)
- —Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach (opens in a new tab)
- —Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approach (opens in a new tab)
202314 papers
- —BEAR-Data: Analysis and Applications of an Open Multizone Building Dataset (opens in a new tab)
- —BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning (opens in a new tab)
- —Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow (opens in a new tab)
- —End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning (opens in a new tab)
- —Energy Storage Price Arbitrage via Opportunity Value Function Prediction (opens in a new tab)
- —GridViz: a Toolkit for Interactive and Multi-Modal Power Grid Data Visualization (opens in a new tab)
- —KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems (opens in a new tab)
- —Neural Operators for Hyperbolic PDE Backstepping Feedback Laws (opens in a new tab)
- —Neural operators for hyperbolic pde backstepping kernels (opens in a new tab)
- —Operator Learning for Nonlinear Adaptive Control (opens in a new tab)
- —Optimal pricing to manage EV charging power in bilevel power-transportation networks (opens in a new tab)
- —Predicting Strategic Energy Storage Behaviors
Where alumni went1 listed
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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