Segment Any Cell
A SAM-based auto-prompting framework for nuclei segmentation with a live demo and broad biomedical imaging relevance.
01 / Selected Publications
Representative work
Three entry points for visitors who want the short version before reading the full publication list.
02 / Research Themes
Three through-lines
The page now reads like an academic dossier: fewer hidden hover details, clearer hierarchy, and stronger research positioning.
I
Computational biology
Protein binding prediction, antibody-antigen modeling, and molecular design systems that connect model performance to biological utility.
II
Biomedical foundation models
Fine-tuning and prompting strategies that adapt general visual models to microscopy, pathology, and other biomedical data.
III
Geometric multimodal learning
Hyperbolic geometry and multimodal alignment for representation learning across images, text, video, and clinical signals.
03 / Projects
Built systems
Projects are presented as a compact ledger rather than a dashboard, so the research narrative stays in front.
Segment Any Cell
Interactive demo for SAM-based cell segmentation with auto-prompting for nuclei segmentation benchmarks.
jaxrie
JAX library for hyperbolic neural networks, including geometry operations and model components.
nadl
Deep learning framework built on JAX and Equinox for research experiments and reusable model utilities.
Nasy's Emacs
Modern Emacs configuration for research programming, writing, navigation, and daily developer workflows.
04 / Skills
Technical range
A concise skill section supports the dossier without competing with the research story.
Expert
PythonJAXEquinoxPyTorchNumPy
Proficient
TensorFlowKerasEmacs LispLLM tooling
Familiar
HaskellJavaScriptC/C++
05 / Bibliography
Publications
The full list stays complete, but the surrounding layout gives it more breathing room and better scanability.
- Saiyang Na, Junzhou Huang, (2026), "Deep learning for TCR–pMHC binding prediction", Deep Learning in Drug Design, 381-402 Book Chapter DOIhttps://doi.org/10.1016/B978-0-44-332908-1.00029-5Copy
- Saiyang Na, Yuzhi Guo, Feng Jiang, Hehuan Ma, Jian Gao, Junzhou Huang, (2025), "Segment any cell: A sam-based auto-prompting fine-tuning framework for nuclei segmentation", IEEE Transactions on Neural Networks and Learning Systems Journal [68 citations] arXivhttps://arxiv.org/abs/2401.13220Copy
- Thao M Dang, Qifeng Zhou, Yuzhi Guo, Hehuan Ma, Saiyang Na, TB Dang, Jian Gao, Junzhou Huang, (2025), "Abnormality-aware multimodal learning for WSI classification", Frontiers in Medicine 12, 1546452 Journal [8 citations] DOIhttps://doi.org/10.3389/fmed.2025.1546452Copy
- Qifeng Zhou, Thao M Dang, Wenliang Zhong, Yuzhi Guo, Hehuan Ma, Saiyang Na, Hao Li, Junzhou Huang, (2025), "HOMIE: Histopathology Omni-modal Embedding for Pathology Composed Retrieval", arXiv preprint arXiv:2502.07221 Preprint [2 citations] arXivhttps://arxiv.org/abs/2502.07221Copy
- Bing Song, Kaiwen Wang, Saiyang Na, Jingxuan Yao, Fatemeh J Fattah, Amber L Martin, Muhammad S von Itzstein et al., (2025), "Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes", Nature Cancer 6, 1570-1584 Journal [4 citations] DOIhttps://doi.org/10.1038/s43018-025-01001-5Copy
- Qifeng Zhou, Thao M Dang, Yuzhi Guo, Hehuan Ma, Wenliang Zhong, Saiyang Na, Jian Gao, Junzhou Huang, (2025), "Contrastive Pretraining for Computational Pathology with Visual-Language Models", 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), 1-4 Conference [2 citations] IEEEhttps://ieeexplore.ieee.org/abstract/document/10981180Copy
- Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na, Wenliang Zhong, Yikang Han, Tao Wang, Junzhou Huang, (2024), "GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity", Briefings in Bioinformatics 25 (4), bbae343 Journal [22 citations] Articlehttps://academic.oup.com/bib/article/25/4/bbae343/7713742Copy
- Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na, Wenjie An, Bing Song, Yikang Han, Jian Gao, Tao Wang, Junzhou Huang, (2024), "AlphaEpi: Enhancing B Cell Epitope Prediction with AlphaFold 3", Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Conference [10 citations] ACM DLhttps://dl.acm.org/doi/10.1145/3698587.3701389Copy
- Thao M Dang, Yuzhi Guo, Hehuan Ma, Qifeng Zhou, Saiyang Na, Jian Gao, Junzhou Huang, (2024), "MFMF: multiple foundation model fusion networks for whole slide image classification", Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Conference [7 citations] ACM DLhttps://dl.acm.org/doi/10.1145/3698587.3701372Copy
- Bing Song, Kaiwen Wang, Saiyang Na, Jingxuan Yao, Fatemeh J Fattah, Muhammad S von Itzstein, David M Yang, Jialiang Liu et al., (2024), "Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data", bioRxiv Preprint [1 citation] bioRxivhttps://www.biorxiv.org/content/10.1101/2024.06.27.601035v2Copy
- Bing Song, Kaiwen Wang, Saiyang Na, Jingxuan Yao, Fatemeh J Fattah, Muhammad S von Itzstein, David M Yang, Jialiang Liu et al., (2024), "An Artificial Intelligence Model for Profiling the Landscape of Antigen-binding Affinities of Massive BCR Sequencing Data", bioRxiv, 2024.06.27.601035 Preprint bioRxivhttps://www.biorxiv.org/content/10.1101/2024.06.27.601035v2Copy
- Lu Zhang, Saiyang Na, Tianming Liu, Dajiang Zhu, Junzhou Huang, (2023), "Multimodal deep fusion in hyperbolic space for mild cognitive impairment study", International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) Conference [12 citations] Springerhttps://link.springer.com/chapter/10.1007/978-3-031-43904-9_65Copy
- Xinyue Ye, Jiaxin Du, Xi Gong, Saiyang Na, Wangjun Li, Shwetha Kudva, (2021), "Geospatial and semantic mapping platform for massive COVID-19 scientific publication search", Journal of Geovisualization and Spatial Analysis 5 (1), 5 Journal [24 citations] DOIhttps://doi.org/10.1007/s41651-021-00073-yCopy