Saiyang Na, Ph.D.

AI Machine Learning Scientist at Sanford Laboratories for Innovative Medicines. Computer Science Ph.D. with expertise in deep learning, LLMs, and computational biology. Creator of open-source JAX and Python libraries.

Education

Publications

• Saiyang Na, Feng Jiang, Qifeng Zhou, Wenliang Zhong, Thao M. Dang, Yuzhi Guo, Hehuan Ma, et al., (2026), “Hyperbolic Gramian Volumes for Multimodal Alignment”, CVPR.
• Saiyang Na, (2026), “Multimodal Deep Learning for Biological Data Understanding”, Ph.D. Dissertation, University of Texas at Arlington.
• Saiyang Na, Junzhou Huang, (2026), “Deep learning for TCR–pMHC binding prediction”, Deep Learning in Drug Design, 381-402.
• Saiyang Na, Yuzhi Guo, Feng Jiang, Hehuan Ma, Jean Gao, Junzhou Huang, (2025), “Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation”, IEEE TNNLS.
• Bing Song, Kaiwen Wang, Saiyang Na, Jia Yao, Farjana J. Fattah, Alexandra L. Martin, Mitchell 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.
• Thao M. Dang, Qifeng Zhou, Yuzhi Guo, Hehuan Ma, Saiyang Na, Thao Bich Dang, Jean Gao, Junzhou Huang, (2025), “Abnormality-aware multimodal learning for WSI classification”, Front. Med. 12, 1546452.
• 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:2502.07221.
• Qifeng Zhou, Thao M. Dang, Yuzhi Guo, Hehuan Ma, Wenliang Zhong, Saiyang Na, Jean Gao, Junzhou Huang, (2025), “Contrastive Pretraining for Computational Pathology with Visual-Language Models”, IEEE ISBI.
• Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na, Weizhi An, Bing Song, Yi Han, Jean Gao, Tao Wang, Junzhou Huang, (2024), “AlphaEpi: Enhancing B Cell Epitope Prediction with AlphaFold 3”, ACM BCB.
• Thao M. Dang, Yuzhi Guo, Hehuan Ma, Qifeng Zhou, Saiyang Na, Jean Gao, Junzhou Huang, (2024), “MFMF: multiple foundation model fusion networks for whole slide image classification”, ACM BCB.
• Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na, Wenliang Zhong, Yi Han, Tao Wang, Junzhou Huang, (2024), “GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity”, Brief. Bioinform. 25 (4), bbae343.
• Lu Zhang, Saiyang Na, Tianming Liu, Dajiang Zhu, Junzhou Huang, (2023), “Multimodal deep fusion in hyperbolic space for mild cognitive impairment study”, MICCAI, Oral.
• Xinyue Ye, Jiaxin Du, Xi Gong, Saiyang Na, Weimin Li, Sonali Kudva, (2021), “Geospatial and semantic mapping platform for massive COVID-19 scientific publication search”, J. Geoviz. Spat. Anal. 5 (1), 5.

Experience

• My current work at Sanford Labs focuses on RNA inverse folding and mRNA translational efficiency.

• Developed HyperGRAM, extending Gramian volume-based multimodal alignment to hyperbolic space using Lorentz model. Source code: github.com/uta-smile/HyperGram.
• Achieved SOTA zero-shot performance on video-text retrieval: MSR-VTT (56.6%), ActivityNet (58.2%), VATEX (79.9%).

• Achieved state-of-the-art performance in multi-step retrosynthesis on the USPTO dataset by combining GNN and LLM, building upon the LocalRetro framework.
• Participated in the Standard Industries Chemical Innovation Challenge on HeroX, a retrosynthesis competition, and advanced to the semi-finals, placing in the top 10.
• Extended research to the Pistachio dataset for industrial-scale retrosynthesis prediction.

• Developed auto-prompt generation pipeline for SAM achieving SOTA performance in cell segmentation.
• Created live demo at https://segment-any-cell.com.
• Published in IEEE TNNLS (2025).

• Developed contrastive learning framework for TCR-pMHC binding prediction with SOTA accuracy.
• Built end-to-end protein sequence-to-structure pipeline integrating RoseTTAFold.
• Published in top journals including Nature Cancer (2025) and Brief. Bioinform. (2024).
• Authored book chapter on deep learning for TCR-pMHC binding prediction in Deep Learning in Drug Design (2026).
• Open-source implementations: GTE (https://github.com/uta-smile/GTE) and Cmai (https://github.com/ice4prince/Cmai).

• Applied hyperbolic graph neural networks to MCI classification, achieving 92.30% accuracy.
• Published in MICCAI 2023 (Oral presentation). Source code: https://github.com/nasyxx/MDF-HS.
• Developed JAX-based hyperbolic neural networks library: https://github.com/nasyxx/jaxrie.

• Performed zebrafish juvenile heart segmentation using nnNet.
• Code: https://github.com/nasyxx/zebrafish_seg

• Analysis of the evolution process of Heterogeneous Graphs in deep learning, on the graph-related tasks, such as node classification tasks.

• Implemented 2D/3D UNet architectures with PyTorch and TensorFlow.
• Developed efficient data pipeline reducing processing time.
• Created TPU-optimized data augmentation techniques for medical images.

• Source: https://github.com/nasyxx/cord_search
• Related paper:
  • Xinyue Ye et al., “Geospatial and semantic mapping platform for massive COVID-19 scientific publication search”, Journal of Geovisualization and Spatial Analysis
  • https://doi.org/10.1007/s41651-021-00073-y
• Build a web app to search for covid-19 related papers.

Open-Source Projects

• A deep learning framework based on JAX and Equinox, which includes custom model implementations, JAX and GPU-compatible dataloaders, an Equinox trainer, and various JAX-based helper utilities.

• A JAX hyperbolic neural networks library.

• An IPython Extensions, which includes a better trace exception and auto performance process and CPU timer.

Teaching and Researching Assistance

• Research Assistance of Dr. Xinyue Ye, 2019 to 2020
• Research Assistance of Dr. Junzhou Huang, start from 2025
• CSE1310, Introduction to Computers & Programming, Fall 2021, Spring 2022, Fall 2022
• CSE5311, Design and Analysis of Algorithms, Fall 2023, Fall 2024
• CSE5324, Software Engineering, Summer 2022
• CSE5334, Data Mining, Summer 2024
• CSE6392, Special Topics in Deep Learning, Spring 2024

Skills

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

Internship

• Our team mapped the route and found the coincident points. We analyzed the right path, corrected the real-time direction to match the track and the bus's designated route, and realized the bus's real-time position.

• Participated in Establishing real time advertisement/commercial bidding (RTB) model.
• Built target people labeling system and made classification with logistic regression.
• The model was well applied into company's practices.