N Nasy Land

Research dossier

Saiyang Na Nasy

AI Machine Learning Scientist at Sanford Laboratories for Innovative Medicines · Ph.D. in Computer Science.

I build learning systems for biological data: cell segmentation, immune receptor binding, molecular design, and geometry-aware multimodal representation learning.

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01 / Selected Publications

Representative work

Three entry points for visitors who want the short version before reading the full publication list.

IEEE TNNLS2025

Segment Any Cell

A SAM-based auto-prompting framework for nuclei segmentation with a live demo and broad biomedical imaging relevance.

arXiv
Nature Cancer2025

Antigen-binding affinity profiling

Deep learning for B cell repertoire analysis and immune-checkpoint inhibitor treatment outcome prediction.

DOI
CVPR2026

Hyperbolic Gramian volumes

HyperGRAM extends Gramian volume-based multimodal alignment to hyperbolic space, with state-of-the-art zero-shot video-text retrieval.

Scholar

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.

TCR-pMHCBCR-antigenRetrosynthesis
II

Biomedical foundation models

Fine-tuning and prompting strategies that adapt general visual models to microscopy, pathology, and other biomedical data.

SAMWSIPathology VLMs
III

Geometric multimodal learning

Hyperbolic geometry and multimodal alignment for representation learning across images, text, video, and clinical signals.

HyperGRAMMICCAI OralJAX

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.

PyTorchSAMGradio

Visit demo

jaxrie

JAX library for hyperbolic neural networks, including geometry operations and model components.

JAXEquinoxHyperbolic geometry

GitHub

nadl

Deep learning framework built on JAX and Equinox for research experiments and reusable model utilities.

JAXEquinoxOptax

GitHub

Nasy's Emacs

Modern Emacs configuration for research programming, writing, navigation, and daily developer workflows.

Emacs LispOrg-modeLSP

Visit site

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.

  1. Saiyang Na, Feng Jiang, Qifeng Zhou, Wenliang Zhong, Thao M Dang, Yuzhi Guo, Hehuan Ma, et al., (2026), "Hyperbolic Gramian Volumes for Multimodal Alignment", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Conference Scholarhttps://scholar.google.com/citations?view_op=view_citation&hl=en&user=rkvOcNEAAAAJ&citation_for_view=rkvOcNEAAAAJ:Se3iqnhoufwCCopy
  2. Saiyang Na, (2026), "Multimodal Deep Learning for Biological Data Understanding", Ph.D. Dissertation, University of Texas at Arlington Dissertation Scholarhttps://scholar.google.com/citations?view_op=view_citation&hl=en&user=rkvOcNEAAAAJ&citation_for_view=rkvOcNEAAAAJ:roLk4NBRz8UCCopy
  3. 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
  4. 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 [73 citations] arXivhttps://arxiv.org/abs/2401.13220Copy
  5. 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 [10 citations] DOIhttps://doi.org/10.3389/fmed.2025.1546452Copy
  6. Qifeng Zhou, Wenliang Zhong, Thao M Dang, Hehuan Ma, Saiyang Na, Yuzhi Guo, Junzhou Huang, (2025), "HOMIE: Histopathology Omni-modal Embedding for Pathology Composed Retrieval", arXiv preprint arXiv:2502.07221 Preprint [4 citations] arXivhttps://arxiv.org/abs/2502.07221Copy
  7. 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
  8. 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 [3 citations] IEEEhttps://ieeexplore.ieee.org/abstract/document/10981180Copy
  9. 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 [24 citations] Articlehttps://academic.oup.com/bib/article/25/4/bbae343/7713742Copy
  10. 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
  11. 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 [8 citations] ACM DLhttps://dl.acm.org/doi/10.1145/3698587.3701372Copy
  12. 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
  13. 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
  14. 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 [14 citations] Springerhttps://link.springer.com/chapter/10.1007/978-3-031-43904-9_65Copy
  15. 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