About Me

I am currently a Postdoctoral Fellow in the Department of Statistics and Data Science, The Chinese University of Hong Kong (CUHK), supervised by Prof. Zhixiang Lin . My postdoctoral research focuses on integrating multi-omics with computational pathology to study cancer diagnosis, tumor heterogeneity, and interpretable multimodal modeling.

I received my M.Sc. degree in Statistics from the School of Mathematics and Statistics, Lanzhou University. I then obtained my Ph.D. in Computer Science and Technology from the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, under the supervision of Prof. Yongbing Zhang .

During my Ph.D., I was awarded the China Scholarship Council (CSC) scholarship and conducted research at the Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V., Germany, working with Dr. Jianxu Chen . My doctoral research focused on computational pathology, including nuclei instance segmentation, multimodal large language model, and visual reasoning for cancer diagnosis.

I also maintain a collection of online Reading Notes and Figure Library , where I curate reading notes, key figures, and thematic summaries spanning computer vision, machine learning, multi-omics, and biomedical foundations for long-term study.

News

• 🔥
  Spring 2026

  Will join The Chinese University of Hong Kong as a Postdoctoral Fellow in the Department of Statistics and Data Science.
• 🎉
  May 2025

  Paper “The Four Color Theorem for Cell Instance Segmentation” accepted at International Conference on Machine Learning (ICML 2025).
• 🇩🇪
  Sep. 2024

  Began joint Ph.D. training at Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V..

Research Experience

Postdoctoral Fellow, Multi-omics Integration

The Chinese University of Hong Kong

Integrating statistical modeling, computational pathology, and multi-omics for cancer research.

2026 – Present

Joint Ph.D. Trainee, Computational Pathology

Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V., Dortmund

Working on multimodal large language models and visual reasoning.

2024 – 2025

Visiting Ph.D. Student, Computer Vision

Tsinghua Shenzhen International Graduate School

Research training in computer vision and computational pathology during the doctoral period.

2021 – 2022

Educations

Ph.D. in Computer Science and Technology

Harbin Institute of Technology, Shenzhen

Dissertation focused on computational pathology, nuclei instance segmentation, and cancer-related image analysis.

2021 – 2025

M.Sc. in Applied Statistics

Lanzhou University

Training in machine learning and deep learning, with a particular focus on graph neural networks and time-series prediction.

2018 – 2021

B.M. in Agricultural and Forestry Economics

Beijing Forestry University

Early training in economics and statistics, with exposure to data, systems thinking, and applied quantitative analysis.

2013 – 2017

Publications

A complete list of my publications can be found on my Google Scholar .

📘 Journal Papers

1. Zhang Y, Cai L, Wang Z, et al. SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation . IEEE Journal of Biomedical and Health Informatics (JBHI), 2025.
2. Zhang Y, Wang Y, Fang Z, et al. DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation . IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024.
3. Cai L, Huang S, Zhang Y, et al. AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes . Medical Image Analysis (MIA), 2025.

📗 Conference Papers

1. Zhang Y, Zhou Y, Wang Y, et al. The Four Color Theorem for Cell Instance Segmentation . ICML 2025.
2. Zhang Y, Fang Z, Wang Y, et al. Category Prompt Mamba Network for Nuclei Segmentation and Classification . AAAI 2025.
3. Guan X, Zhang Z, Zhang Y, et al. OT-StainNet: Optimal Transport-Driven Semantic Matching for Weakly Paired H&E-to-IHC Stain Transfer . AAAI 2025.
4. Zhang L, Sun B, Zhang Y, et al. Counting by Points: Density-Guided Weakly-Supervised Nuclei Segmentational . ACM MM 2025 (Oral).
5. Zhang Y, Guan X, Li H, et al. Multi-Scale Context Intertwining for Panoramic Renal Pathology Segmentation . ICASSP 2025.
6. Guan X, Zhang Z, Zhang Y, et al. Correlated Multiple IHC Virtual Staining for Breast Histopathological Images . ICASSP 2025.
7. Zhang L, Zhang Y, Cai L, et al. Relation-Aware Graph Attention Network for Nuclei Classification . ICME 2025 (Oral).
8. Zhang Y, Wang Z, Wang Y, et al. Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation . MIDL 2024 (Oral).
9. Wang Z, Zhang Y, Cai L, et al. Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation . MICCAI 2024.
10. Fang Z, Wang Y, Zhang Y, et al. Mammil: Multiple Instances Learning for Whole Slide Images with State Space Models . BIBM 2024.

Awards

Projects

Skills

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