Kunyu Zhang

Kunyu Zhang

Deep Learning, AI4Neuroscience and AI4Health

🔥 News

👨‍🔬 About Me

I am an aspiring researcher passionate about harnessing deep learning to drive innovation in healthcare and medicine. My work focuses on applying deep learning to address complex medical challenges and improve patient outcomes.

Currently, I am exploring a range of AI applications in healthcare, including medical image analysis, clinical decision support systems, and predictive modeling for disease diagnosis and treatment. Beyond medicine, I am also committed to developing flexible, efficient, and generalizable deep learning methodologies applicable across diverse scientific domains.

📚 Publications

* Equal contribution

[KDD 26] USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation
[Paper]
Yingxu Wang*, Kunyu Zhang*, Mengzhu Wang, Siyang Gao, Nan Yin
We propose USBD, a source-free graph domain adaptation framework that distills source graphs into a compact universal structural basis spanning diverse topological patterns, then uses a spectral-aware ensemble to improve adaptation under severe structural shifts.

[MICCAI 25] MvHo-IB: Multi-View High-Order Information Bottleneck for Brain Disorder Diagnosis
[Paper] [Code]
Kunyu Zhang, Qiang Li, Shujian Yu
We propose a novel method to capture higher-order interactions while using information bottleneck to reduce redundancy between multi-view information for brain disorder diagnosis.

[Briefings in Bioinformatics] ProtoMol: Enhancing Molecular Property Prediction via Prototype-Guided Multimodal Learning
[Paper] [Code]
Yingxu Wang*, Kunyu Zhang*, Jiaxin Huang, Nan Yin, Siwei Liu, Eran Segal
We propose ProtoMol, a prototype-guided multimodal framework that hierarchically aligns molecular graphs and textual descriptions via layer-wise cross-modal attention and a shared prototype space, consistently outperforming prior methods on molecular property prediction while improving interpretability.

🔬 Research Interests

  • AI in Biomedicine: Applying artificial intelligence across biomedical domains, including but not limited to neuroscience, medical imaging, clinical diagnosis, and prognosis

  • Machine Learning for Health: Investigating the adaptability and limitations of machine learning methods in biomedical research

  • Generalizable AI Methodologies: Exploring the theoretical foundations for extending AI from biomedical applications to other scientific disciplines through flexible, low-cost, and efficient deep learning frameworks