Scholar
Zhongyi Han
Google Scholar ID: 0J-PErUAAAAJ
Professor, Shandong University
Citations & Impact
Citations
2,445
H-index
19
i10-index
28
Publications
20
Co-authors
35
list available
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- - Published over 40 papers in top-tier journals and conferences, including TPAMI, IJCV, TIP, TKDE, MLJ, TMI, MedIA, NeurIPS, CVPR, AAAI, IJCAI, IPMI.
- - Holds six granted invention patents, with two papers selected as ESI Highly Cited Papers.
- - Received more than 2,500 citations on Google Scholar.
- - Serves as a reviewer or senior program committee member for leading journals and top-tier AI conferences, including TPAMI, IJCV, TIP, TNNLS, ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV.
- - Honors include the Second Prize of the Qingdao Science and Technology Progress Award (2023), the CCF Doctoral Dissertation Incentive Program in Artificial Intelligence and Pattern Recognition (2025), the Shandong Provincial Excellent Doctoral Dissertation Award (2024), the ACM (Jinan) Excellent Doctoral Dissertation Award (2023), and recognitions from the Shandong Artificial Intelligence Society for outstanding doctoral and master's theses.
- - Named an Outstanding Reviewer for NeurIPS 2024 and IEEE TMI 2022.
- - Participated in nearly ten national and provincial research projects, including the National Natural Science Foundation of China (Key and General Programs), the National Key R&D Program, and major basic research projects in Shandong Province.
Research Experience
- - July 2025 to present: Professor at the School of Software, Shandong University
- - April 2023 to January 2024: Postdoc Researcher at the Machine Learning Department, MBZUAI
- - January 2024 to July 2025: Postdoc Researcher at The Center of Excellence for Generative AI, KAUST
Education
- No content
Background
- His research interests include machine learning, foundation models, agentic AI, AI for Science, and medical image analysis. He focuses on fundamental research in robust machine learning in open environments, addressing key challenges such as data distribution shift, class distribution shift, and limited labeled data.
Miscellany
- No information provided about personal interests and other details
