About Me
Ph.D. candidate supervised by Dr. Junzhou Huang at UT Arlington. Research focus: deep learning for computational biology and multimodal learning.
16
Publications
- 5 Journal Articles
- 5 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
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
- HyperGRAM
56.6%/58.2%/79.9% R@1
Video-text retrieval SOTA - Hyperbolic MCI
92.26% accuracy MICCAI Oral - jaxrie
JAX hyperbolic neural networks library
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
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
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
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
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
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
Skills
Expert Python, JAX with Equinox, PyTorch, NumPy
Proficient TensorFlow with Keras, Lisp (Emacs Lisp), LLM
Familiar Haskell, JavaScript, C/C++
Publications
- 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 [61 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 [3 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), "MLLM4PUE: Toward Universal Embeddings in Digital Pathology through Multimodal LLMs", arXiv preprint arXiv:2502.07221 Preprint [3 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 [1 citation] 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 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 [19 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 [7 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 [5 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 [2 citations] 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 [11 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