Department/College/UnitBaskin School of Engineering

TitleAssociate Professor

AffiliationFaculty

Other UCSC Campus AffiliationCenter for Teaching & Learning
Crown College

Web SiteHomepage
Google

Primary Office LocationEngineering Building 2
E2-547B

Additional Location(s)E2-577

Office HoursThursday 4:30-5:30pm for Fall 2025

Mail StopSOE2

Biography, Education and Training

• Yu Zhang is an Associate Professor in the ECE Department of UC Santa Cruz. Prior to joining UCSC, he was a postdoc at UC Berkeley and Lawrence Berkeley National Laboratory. He received the Ph.D. degree in Electrical and Computer Engineering from the University of Minnesota. Dr. Zhang's research interests span the broad areas of smart power grids, optimization theory, deep learning and AI. Dr. Zhang has published in many leading journals in his research areas, including IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Transactions on Sustainable Energy, IEEE Transactions on Signal Processing, and IEEE Transactions on Control of Network Systems. Dr. Zhang received the Hellman Fellowship in 2019. He was the co-recipient of the Early Career Best Paper Award given by the Energy, Natural Resources, and the Environment (ENRE) section of the Institute of Operations Research and the Management Sciences (INFORMS) in 2021, and the recipient of the IISE Energy Systems (ES) Division Outstanding Young Investigator Award in 2025.

Research Interests

Energy systems, smart grids, data centers, wireless networks, AI/ML, optimization, operations research, control, and signal processing

Selected Publications

1. Physics-Informed Gradient Estimation for Accelerating Deep Learning-Based AC-OPF
2. Load Restoration in Islanded Microgrids: Formulation and Solution Strategies
3. Physics-guided Residual Learning for Probabilistic Power Flow Analysis
4. MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data
5. A Unifying Framework of Attention-based Neural Load Forecasting
6. Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
7. Scale- and Context-Aware Convolutional Non-Intrusive Load Monitoring
8. Conic Relaxations for Power System State Estimation with Line Measurements
9. Distributed Stochastic Market Clearing with High-Penetration Wind Power
10. Robust Workload and Energy Management for Sustainable Data Centers
11. Electricity Market Forecasting via Low-Rank Multi-Kernel Learning
12. Robust Energy Management for Microgrids With High-Penetration Renewables