Ze-Feng Gao (高泽峰)
Scholar

Ze-Feng Gao (高泽峰)

Google Scholar ID: vB64k4IAAAAJ
Lecture, Renmin University of China
Inverse Design of MaterialsAI for PhysicsPhysics for AI
Citations & Impact
All-time
Citations
426
 
H-index
11
 
i10-index
12
 
Publications
20
 
Co-authors
0
 
Publications
20 items
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
  • Published over twenty papers in prestigious international conferences and journals such as ACL, NeurIPS, EMNLP, COLING, National Science Review, and Phys.Rev.Research. The work on over-parameterization of pre-trained models using matrix product operators was nominated for Best Paper at ACL2023. Research findings have been cited by experts from institutions like Cambridge University, Stanford University, and Meta. Has led seven research projects in recent years, including National Natural Science Foundation of China (NSFC) Youth Fund, NSFC General Program, NSFC Key Project (sub-project), National Key R&D Program (sub-project), Beijing Natural Science Foundation Interdisciplinary Key Project (sub-project), and three horizontal projects.
Research Experience
  • Since 2021.07, Associate Researcher, Key Laboratory of Quantum Measurement and Control, Ministry of Education, Renmin University of China; 2021.07-2024.06, Postdoctoral Researcher, Renmin University of China, Collaborative Advisors: Prof. Ji-Rong Wen, Prof. Xin Zhao, Prof. Hao Sun.
Education
  • 2016.09-2021.06, Ph.D. in Theoretical Physics, Renmin University of China, Advisor: Prof. Zhong-Yi Lu; 2012.09-2016.06, B.S. in Physics, Renmin University of China.
Background
  • Lecturer at the School of Physics, Renmin University of China. Research interests include numerical methods in quantum physics, pre-trained model compression, and AI-assisted discovery and generation of functional crystal materials. Uses AI methods to assist in the discovery of new functional materials.
Miscellany
  • Email: zfgao@ruc.edu.cn; GitHub; Google Scholar; DBLP; Teaches courses including Introduction to Artificial Intelligence (undergraduate course), Artificial Intelligence and Physics (undergraduate course), Nobel Prizes in Physics and Modern Physics (undergraduate course).
Co-authors
0 total
Co-authors: 0 (list not available)