Scholar
Zhi-Hua Zhou
Google Scholar ID: rSVIHasAAAAJ
Nanjing University
Citations & Impact
Citations
59,813
H-index
91
i10-index
403
Publications
20
Co-authors
179
list available
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- Awards (incomplete):
- - CCF-ACM Artificial Intelligence Award (2020)
- - Nation Science Review 2019 Best Paper Award
- - IEEE Computer Society Edward J. McCluskey Technical Achievement Award (2019)
- - ACML Distinguished Contributions Award (2019)
- - Academy of Europe Foreign Member (2017)
Research Experience
- Main Positions:
- - Cheung Kong Professor (appointed by the Ministry of Education of China), Department of Computer Science & Technology, Nanjing University, China, Mar. 2007 - present
- - Professor, Department of Computer Science & Technology, Nanjing University, China, Nov. 2003 - present
- Administrative Positions:
- - Vice President, Nanjing University, China, Jul. 2024 - present
- - Dean, School of Artificial Intelligence, Nanjing University, China, Mar. 2018 - Sep. 2024
- - Head, Department of Computer Science and Technology, Nanjing University, China, Jan. 2018 - Sep. 2024
- - Deputy Head, Department of Computer Science and Technology, Nanjing University, China, May 2013 - Jan. 2018
- - Standing Deputy Director, National Key Laboratory for Novel Software Technology, Nanjing University, China, Nov. 2010 - present
- - Founding Director, Institute of Machine Learning and Data Mining (LAMDA), Nanjing University, China, Nov. 2007 - present
- - Director, AI Lab, Department of Computer Science & Technology, Nanjing University, China, May 2002 - present
- Honorary Positions:
- - Adjunct Professor, Sichuan University, China, Jan. 2009 - Dec. 2012
- - Adjunct Professor, Hong Kong University of Science & Technology, China, Jan. 2009 - Dec. 2010
- - Adjunct Professor, Nanjing University of Aeronautics and Astronautics, China, Jun. 2004 - May 2008
- - Honorary Fellow, Deakin University, Australia, Nov. 2002 - Oct. 2006
Background
- Research interests include ensemble learning, evolutionary learning, weakly supervised learning (including semi-supervised learning, active learning, multi-instance learning, crowdsourcing learning, learning with noisy labels, etc.), multi-label learning, imbalanced learning (including cost-sensitive learning, class-imbalance learning), learning theory, face recognition, and more. Currently, he is also interested in open-environment machine learning, machine learning + logic reasoning, and Learnware.
