Gexin Huang

Gexin Huang

Multimodal Learning · Vision–Language Models · Generative Models · Computational Pathology

About

I am a Ph.D. student supervised by Xiaoxiao Li at the Trusted and Efficient AI (TEA) Lab, University of British Columbia.

From 2022 to 2023, I was a research assistant supervised by Xiaodan Liang at the Human Cyber Physical Intelligence Integration (HCP) Lab, Sun Yat-Sen University, Guangzhou, China. I received my master’s degree in Pattern Recognition and Intelligent Systems from South China University of Technology. During my master’s studies, I was co-advised by Zhuliang Yu and Wei Wu at the Center for Brain-Computer Interfaces and Information Processing.

My research interests lie in Bayesian learning and machine learning methods for interdisciplinary applications. I am particularly interested in the intersection of deep learning and Bayesian statistics, as well as generative models, explainability, and graph representation learning.

Research Focus

  • Evidence-grounded visual reasoning: benchmarks and reusable evidence representations for reasoning over ultra-resolution images and whole-slide pathology images.
  • Multimodal computational pathology: combining heterogeneous foundation models, biological knowledge, and image features for mutation prediction and survival analysis.
  • Controllable medical image generation: diffusion models for interactive tumor editing and identity-preserving brain aging modeling.

Publications

2026

  1. SlideBank: A Persistent Hierarchical Evidence Bank for Consistent Whole-Slide Reasoning
    Beidi Zhao*, Gexin Huang*, Ciro Zhang*, Anqi Li, Yusheng Tan, Chen Zhou, Gang Wang, Zu-hua Gao, and Xiaoxiao Li.
    2026 · Submitted to WACV. *Equal contribution.

  2. Knowledge-Guided Multi-Modality Transformer for Multi-Label Genetic Mutation Prediction
    Gexin Huang, Chenfei Wu, Mingjie Li, Xiaojun Chang, Ying Sun, Lei Xing, Xiaodan Liang, Liang Lin, Guang Yang, and Shen Zhao.
    Pattern Recognition, 2026. Code

  3. UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning
    Gexin Huang, Yanting Yang, Myeongkyun Kang, Beidi Zhao, Jun Zhou, Chen Zhou, Gang Wang, Zu-hua Gao, and Xiaoxiao Li.
    2026 · Submitted to ICLR.

  4. Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models
    Gexin Huang, Anqi Li, Yusheng Tan, Beidi Zhao, Gang Wang, Zu-hua Gao, and Xiaoxiao Li.
    2026 · Submitted to WACV. Code

2025

  1. Artificial Intelligence-Driven Risk Stratification Reveals Distinct Prognostic Signatures and a Fibronodular Subtype of Hepatocellular Carcinoma
    Gexin Huang, Ali Bashashati, Katherine Rich, Xiaoxiao Li, and Zu-hua Gao.
    2025 · Submitted to European Journal of Cancer.

  2. See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosis
    Ruinan Jin, Gexin Huang, Xinwei Shen, Qiong Zhang, Yan Shuo Tan, and Xiaoxiao Li.
    2025 · Submitted to Medical Image Analysis.

  3. Identity Preserving Latent Diffusion for Brain Aging Modeling
    Gexin Huang, Zhangsihao Yang, Yalin Wang, Guido Gerig, Mengwei Ren, and Xiaoxiao Li.
    2025 · Submitted to Medical Image Analysis.

  4. Interactive Tumor Progression Modeling via Sketch-Based Image Editing
    Gexin Huang, Ruinan Jin, Yucheng Tang, Can Zhao, Tatsuya Harada, Xiaoxiao Li, and Gu Lin.
    2025 · Preprint.

2024

  1. Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder–Decoder Network
    Gexin Huang, Ke Liu, Jiawen Liang, Chang Cai, Zheng Hui Gu, Feifei Qi, Yuanqing Li, Zhu Liang Yu, and Wei Wu.
    IEEE Transactions on Neural Networks and Learning Systems, 2024. Code

Academic Service

  • Reviewer: Pattern Recognition, Medical Image Analysis, IEEE Transactions on Medical Imaging, and MICCAI (2025); CVPR (2023–2025); IEEE Transactions on Neural Networks and Learning Systems (2022–2023).
  • Teaching Assistant: Advanced Techniques in Machine Learning; Digital Signal Processing (2019).