Scholar
Shengyu Tao, PhD
Google Scholar ID: MHzCCogAAAAJ
Tsinghua University; University of California, Berkeley
Citations & Impact
Citations
885
H-index
14
i10-index
21
Publications
20
Co-authors
6
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Published several articles in Nature Catalysis (2023), Nature Communications (2024a; 2024b; 2023), Energy and Environmental Science (2025a; 2025b), National Science Review, ACS Energy Letters, Journal of Energy Chemistry, Journal of Power Sources, and Applied Energy, etc. Peer reviewer for multiple journals and guest editor at Electronics (MPDI) for the special issue 'Advanced Control and AI Methods for Future Battery Diagnostics and Prognostics'.
Research Experience
- Worked as a research intern at Tencent AI Lab and Microsoft Research Asia (MSRA). Currently working at the interface of energy storage, power systems, electrochemistry, and artificial intelligence (AI) at Tsinghua University and UC Berkeley.
Education
- PhD from a joint training program between Tsinghua University and UC Berkeley, supervised by Xuan Zhang, Guangmin Zhou, and Scott Moura.
Background
- Research interests include AI-enabled applications for the sustainable use of retired electric vehicle batteries (reuse and recycling), such as power grid energy storage and critical material recycling. Focuses particularly on state estimation, diagnosis, and prognosis under limited and heterogeneous data availability. Collaborates closely with materials scientists on AI for Science (AI4S) topics, including the diagnosis and prognosis of lithium-sulfur batteries and lithium-metal batteries.
Miscellany
- Personal interests not provided
