Nasy Land

About Me

Ph.D. candidate supervised by Dr. Junzhou Huang at UT Arlington. Research focus: deep learning for computational biology and multimodal learning.

13 Publications
  • 5 Journal Articles
  • 4 Conference Papers
  • 3 Preprints
  • 1 Book Chapter
  • 2 First Author
130+ Citations
  • Segment Any Cell: 61 citations
  • COVID-19 Mapping: 24 citations
  • GTE: 19 citations
  • Hyperbolic MCI: 11 citations
Nature Cancer
Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes
B Song, K Wang, S Na, et al.
Nature Cancer, 2025 DOI
IEEE TNNLS
Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
S Na, Y Guo, F Jiang, H Ma, J Gao, J Huang
IEEE TNNLS, 2025 · 61 citations arXiv
MICCAI Oral
Multimodal Deep Fusion in Hyperbolic Space for Mild Cognitive Impairment Study
L Zhang, S Na, T Liu, D Zhu, J Huang
MICCAI 2023 · 11 citations Springer
IJCAI 2025
Multi-step Retrosynthesis with Reinforcement Learning
S Na, et al.
IJCAI 2025 · HeroX Semi-finalist

Research

🧬

Computational Biology

Deep learning for protein binding prediction & molecular design

Key Projects

  • TCR-pMHC Binding
    +7.1% ROC AUC Nature Cancer Book Chapter
  • BCR-Antigen (Cmai)
    0.907 AUROC, clinical ICI response prediction
  • Retrosynthesis
    +7.1% Top-1 (USPTO), HeroX Semi-finalist IJCAI 2025
🔬

Foundation Models

Adapting foundation models for biomedical imaging

Key Projects

  • Segment Any Cell
    Dice 84.03/93.04 SOTA IEEE TNNLS
  • Pathology VLMs
    Multimodal WSI classification ISBI
  • Live Demo
    segment-any-cell.com
🌐

Multimodal Learning

Hyperbolic geometry for multimodal alignment

Key Projects

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Projects

Segment Any Cell

Interactive demo for SAM-based cell segmentation

Description
Auto-prompting framework that adapts SAM for nuclei segmentation. Achieves SOTA on multiple benchmarks with zero manual prompts.
Tech Stack
PyTorch SAM Gradio
Impact
IEEE TNNLS publication, 61+ citations

jaxrie

JAX library for hyperbolic neural networks

Description
Comprehensive library implementing hyperbolic geometry operations and neural network layers in JAX. Supports Poincaré ball and Lorentz models.
Features
Hyperbolic embeddings, Möbius operations, hyperbolic attention, geodesic computations
Tech Stack
JAX Equinox Python

nadl

Deep learning framework built on JAX and Equinox

Description
Personal deep learning framework featuring modular architecture, custom training loops, and utilities for research experiments.
Features
Data loaders, training utilities, model components, experiment tracking
Tech Stack
JAX Equinox Optax

Nasy's Emacs

Modern Emacs configuration for developers

Description
Comprehensive Emacs configuration with modern UX, featuring LSP integration, Git tools, and language support for Python, JAX, Haskell, and more.
Features
Tree-sitter, Native compilation, Org-mode, LSP, Magit, Vertico/Consult
Tech Stack
Emacs Lisp Org-mode

naipyext

IPython extensions for better debugging

Description
Enhanced IPython extensions with improved traceback formatting, syntax highlighting, and debugging utilities for data science workflows.
Features
Rich tracebacks, auto-reload, magic commands, Jupyter integration
Tech Stack
Python IPython

Research Archive

Collection of research code and experiments

Description
Archive of research implementations including HyperGRAM, retrosynthesis models, and various deep learning experiments.
Contents
Video-text retrieval, molecular design, multimodal learning experiments
Tech Stack
PyTorch JAX Python

Skills

Expert Python, JAX with Equinox, PyTorch, NumPy
Proficient TensorFlow with Keras, Lisp (Emacs Lisp), LLM
Familiar Haskell, JavaScript, C/C++

Publications

  1. 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
  2. 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 [61 citations] arXivhttps://arxiv.org/abs/2401.13220Copy
  3. 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 [3 citations] DOIhttps://doi.org/10.3389/fmed.2025.1546452Copy
  4. Qifeng Zhou, Thao M Dang, Wenliang Zhong, Yuzhi Guo, Hehuan Ma, Saiyang Na, Hao Li, Junzhou Huang, (2025), "MLLM4PUE: Toward Universal Embeddings in Digital Pathology through Multimodal LLMs", arXiv preprint arXiv:2502.07221 Preprint [3 citations] arXivhttps://arxiv.org/abs/2502.07221Copy
  5. 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 [1 citation] DOIhttps://doi.org/10.1038/s43018-025-01001-5Copy
  6. 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 IEEEhttps://ieeexplore.ieee.org/abstract/document/10981180Copy
  7. 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 [19 citations] Articlehttps://academic.oup.com/bib/article/25/4/bbae343/7713742Copy
  8. 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 [7 citations] ACM DLhttps://dl.acm.org/doi/10.1145/3698587.3701389Copy
  9. 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 [5 citations] ACM DLhttps://dl.acm.org/doi/10.1145/3698587.3701372Copy
  10. 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 [2 citations] bioRxivhttps://www.biorxiv.org/content/10.1101/2024.06.27.601035v2Copy
  11. 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
  12. 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 [11 citations] Springerhttps://link.springer.com/chapter/10.1007/978-3-031-43904-9_65Copy
  13. 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