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Dr. Yu Zhang Associate Professor Emerging Leaders Fellow (ELF) Office: E2-547B
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Short Bio
Dr. Yu Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Cruz. He received his Ph.D. in Electrical and Computer Engineering from the University of Minnesota, followed by postdoctoral appointments at the University of California, Berkeley and the Lawrence Berkeley National Laboratory.
Dr. Zhang’s research advances the resilience, efficiency, and sustainability of modern electric power systems through innovations in AI-driven optimization, machine learning, and dynamic decision-making. His work develops physics-aware learning methods, stochastic and robust optimization techniques, and cyber-physical coordination frameworks to support reliable grid operations under uncertainty. Recent projects include learning-augmented outage forecasting, planning for weather-driven grid hardening, and integrating large flexible loads such as data centers into market and operational strategies.
Dr. Zhang has been recognized with multiple awards, including the 2025 Outstanding Young Investigator Award from the Energy Systems Division of the Institute of Industrial and Systems Engineers (IISE), the 2021 Early Career Best Paper Award from the INFORMS Energy, Natural Resources, and the Environment (ENRE) Section, and the 2019 Hellman Fellowship.
News
10/2025: We (with Shourya Bose and Tomas Kaljevic) won the first prize of the 2025 INFORMS Data Mining Society Data Challenge.
10/2025: Sifat-E-Tanzim Chowdhury successfully defended her PhD Thesis “Enhancing the Resilience of Electric Power Systems during Extreme Weather Events.” Congratulations!
10/2025: New paper “Resilient Grid Hardening against Multiple Hazards: An Adaptive Two-Stage Stochastic Optimization Approach” is accepted by Electric Power Systems Research.
09/2025: I attend the panel “The Future of AI & OR Convergence: How AI and OR Together Redefine Decision-Making” at the 2025 INFORMS Regional Analytics Conference San Francisco.
09/2025: I am selected to join the Emerging Leaders Fellows (ELF) program.
09/2025: New paper “Presolving Convexified Optimal Power Flow with Mixtures of Gradient Experts” is accepted by Energy and AI.
09/2025: I am selected to join the inaugural cohort of UCSC Hispanic-Serving Research Institution (HSRI) Fellows program.
08/2025: I gave a lightning talk at the 2025 UC–LLNL Workshop on AI for Power Systems and Smart Grid.
08/2025: I served on the panel for Community Choice Energy and Electricity Market and as a judge for the Student Poster Competition at the 2025 Global Green Development Summit, Stanford University.
07/2025: I gave a talk on Learning to Optimize at ICCOPT 2025.
06/2025: Our proposal (joint with Dr. Ashesh Chattopadhyay) got funded by the BSOE Seed Funding for Interdisciplinary Collaborative Research.
06/2025: I gave a keynote at the 2nd International Conference on Artificial Intelligence and Machine Learning (AIM-2025 Hybrid).
05/2025: I am selected to join the UCSC TLC Faculty Fellows 2025-26 cohort.
05/2025: Shourya Bose won the Best Presentation of Baskin Engineering at UCSC 2025 Graduate Research Symposium.
04/2025: I am elected to the IISE Energy Systems Division Board.
03/2025: I am honored to receive the IISE Energy Systems Division Outstanding Young Investigator Award (Second Place)!
03/2025: We received the Frontier Fellowship Award! The proposed research will be conducted in collaboration with Profs. Jason Eshraghian and Shaowei Chen, with Hafsah Mahmood as the student mentee.
03/2025: New paper “A Hybrid Reinforcement Learning and Optimization Framework for Critical Load Restoration” is accepted by Electric Power Systems Research.
02/2025: Kejun Chen successfully defended her PhD Thesis “Power Flow Analysis and Optimal Power Flow with Physics-Informed Deep Learning.” Congratulations!
02/2025: I was invited to give a talk at the Stanford Smart Grid Seminar: “Accelerating AC-OPF with Deep Learning”.
02/2025: New paper Physics-Informed Gradient Estimation for Accelerating Deep Learning based AC-OPF is accepted by IEEE Transactions on Industrial Informatics.
01/2025: New paper “Enhanced Wildfire Detection Using a Mixture of Experts Approach” is accepted by Journal of Student Research.
10/2024: New paper From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption is accepted by NeurIPS Workshop.
10/2024: Gabriel Intriago won the CPSRC dissertation award.
06/2024: Gabriel Intriago successfully defended his PhD Thesis “Advanced Anomaly Detection Techniques for Cyber-physical Power Systems.” Congratulations!
04/2024: Shourya Bose won the 2024-25 Dissertation-Year Fellowship.
04/2024: New paper Two-stage Stochastic Optimal Power Flow for Microgrids with Uncertain Wildfire Effects is accepted by IEEE Access.
04/2024: New paper Addressing Heterogeneity in Federated Load Forecasting via Personalization Layers is accepted by the Institute of Industrial and Systems Engineers (IISE) Annual Conference.
03/2024: New paper Greening Service Capacity in Telecom Supply Chain under Environmental Regulation is accepted by Sustainability.
03/2024: New paper Adaptive Fault Detection for Grid-Forming Inverters is accepted by IEEE Access.
02/2024: New paper A Novel Observer-Centric Approach for Detecting Faults in Islanded AC Microgrids with Uncertainties is accepted by IEEE Systems Journal.
02/2024: Privacy-Preserving Load Forecasting via Personalized Model Obfuscation is accepted by 2024 IEEE PES General Meeting
12/2023: IEEE Spectrum News on our work of Reinforcement Learning based Load Restoration; see more Media Coverage.
12/2023: New preprint Learning to Optimize: Accelerating Optimal Power Flow via Data-driven Constraint Screening.
12/2023: Jing Xiong successfully defended her PhD Thesis “Deep Learning Based Load Forecasting and Monitoring for Electric Power Systems.” Congratulations!
11/2023: New preprint Privacy-Preserving Load Forecasting via Personalized Model Obfuscation.
09/2023: Our team won the L2RPN Delft 2023 competition.
09/2023: Our paper Load Restoration in Islanded Microgrids: Formulation and Solution Strategies is accepted by IEEE Transactions on Control of Network Systems.
08/2023: Our paper Physics-guided Residual Learning for Probabilistic Power Flow Analysis is accepted by IEEE Access.
07/2023: Our paper MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data is accepted by IEEE Transactions on Industrial Informatics.
07/2023: Our paper “Power System Quasi-Steady State Estimation: An Echo State Network Approach” is accepted by the 55th Annual North American Power Symposium.
06/2023: Shourya Bose is awarded the IEEE-IES Student and Young Professionals Paper Assistances (IES-SYPA).