Possible Rotation Projects:
- Spatial transcriptomics analysis of disease progression and immune microenvironment
- AI-driven workflows for spatial transcriptomics data analysis
- Uncovering dysregulation of tissue-conserved m6A RNA methylation in cancer
Training Technologies Used:
- LLM, AI agents, spatial transcriptomics data analysis (ScanPy, SquidPy, Seurat), ML/statistical modeling, deep learning
- B.S., Applied Electronics, Northwestern Polytechnic University-1995
- M.S., Electrical Engineering, The State University of New York at Stony Brook-1997
- Ph.D., Electrical Engineering, The State University of New York at Stony Brook-2001
Meng, W., Das, A., Sinha, H., Naous, R., Bracci, P.M., McGrath, M., Huang, Y.* and Gao, S.J.*, 2025. Spatial Single-Cell Atlas Reveals KSHV-Driven Broad Cellular Reprogramming, Progenitor Expansion, Immune and Vascular Remodeling in Kaposi’s Sarcoma. bioRxiv.
Liu, Z., Das, A., Meng, W., Chiu, Y.C., Gao, S.J. and Huang, Y.*, 2025. ST2HE: A Cross-Platform Framework for Virtual Histology and Annotation of High-Resolution Spatial Transcriptomics Data. arXiv preprint arXiv:2510.12840.
Hasib, M.M., Jo, S., Sinha, H., Song, J., Das, A., Liu, Z., Galloway, H., Huang, H., Zhang, K., Gao, S.J. and Chiu, Y.C., Li, L., Huang, Y.* 2025. A Process-Centric Survey of AI for Scientific Discovery Through the EXHYTE Framework.
Sivarajkumar, S., Edupuganti, S., Lazris, D., Bhattacharya, M., Davis, M., Dressman, D., Thomas, R., Hu, Y., Ren, Y., Xu, H. and Yang, P., Huang, Y*, Wang, Y* 2026. Extraction of Treatments and Responses From Non–Small Cell Lung Cancer Clinical Notes Using Natural Language Processing. JCO clinical cancer informatics, 10, p.e2500138.
Officer, A., Meng, W., Spellman, D., Martin, J., Bracci, P.M., McGrath, M., Huang, Y.* and Gao, S.J.*, 2025. Oral Microbiome and Inferred Functions Predict Kaposi's Sarcoma Progression. Journal of Medical Virology, 97(10), p.e70647.
Lai, Y.J., Wang, L.J., Yasaka, T.M., Shin, Y., Ning, M., Tan, Y., Shih, C.H., Guo, Y., Chen, P.Y., Galloway, H., Liu, Z., Das, A., Tseng, G.C., Monga, S.P., Huang, Y.*, Chiu, Y.* 2025. Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models. Cancer Research Communications, 5(4), pp.706-718.
Zou, Y., Ahsan, M.U., Chan, J., Meng, W., Gao, S.J., Huang, Y. and Wang, K., 2025. A Comparative Evaluation of Computational Models for RNA modification detection using Nanopore sequencing with RNA004 Chemistry. Briefings in Bioinformatics, Volume 26, Issue 4, bbaf404
Ruffin, A.T., Casey, A.N., Kunning, S.R., MacFawn, I.P., Liu, Z., Arora, C., Rohatgi, A., Kemp, F., Lampenfeld, C., Somasundaram, A. Rappocciolo, G., Kirkwood, J. M., Duvvuri, U., Seethala, R., Bao, R., Huang, Y., Cillo, A. R., Ferris, R.T., Bruno, T.C., 2025. Dysfunctional CD11c− CD21− extrafollicular memory B cells are enriched in the periphery and tumors of patients with cancer. Science translational medicine, 17(786), p.eadh1315.
Das A, Meng W, Liu Z, Hasib MM, Galloway H, da Silva SR, Chen L, Sica GL, Paniz-Mondolfi A, Bryce C, Grimes Z, Sordillo EM, Cordon-Cardo C, Rivera KP, Flores M, Chiu YC, Huang Y*, Gao SJ.* Molecular and immune signatures, and pathological trajectories of fatal COVID‐19 lungs defined by in situ spatial single‐cell transcriptome analysis. J Med Virol. 2023 Aug;95(8):e29009. PMID: 37563850 PMCID: PMC10442191.
Tan B, Liu H, Zhang S, da Silva SR, Zhang L, Meng J, Cui X, Yuan H, Sorel O, Zhang S, *Huang Y, *Gao SJ. Viral and Cellular N6-Methyladenosine (m6A) and N6, 2′-O-Dimethyladenosine (m6Am) Epitranscriptomes in KSHV Life Cycle. Nat Microbiol. 2018 Jan;3(1):108-120. PMID: 29109479 PMCID: PMC6138870.
Dr. Huang's research focuses on developing and applying artificial intelligence and machine learning methods to unravel complex disease mechanisms and advance precision oncology. His work spans deep learning for multi-omics data integration, natural language processing for clinical knowledge extraction, and large language model-enabled biological knowledge graph construction. By building accessible, end-to-end AI toolkits, his lab translates complex genomic and clinical data into actionable therapeutic insights.
A major thrust of his research is the development of spatial transcriptomics and single-cell analysis methods to study disease in its native tissue context. His lab builds AI-powered workflows for data analysis and hypothesis generation, frameworks for virtual histology, cell type annotation, and tissue structure characterization from high-resolution spatial transcriptomics data. These tools have been applied to generate spatial single-cell atlases of Kaposi's sarcoma, zika infection of mouse brains, and fatal COVID-19 lungs, revealing how pathogens reprogram host cells, remodel the immune microenvironment, and drive disease progression.
Dr. Huang also leads a sustained research program in m6A epitranscriptomics, where his lab has developed widely adopted computational pipelines for transcriptome-wide m6A mapping, differential analysis, and functional prioritization. Tools such as exomePeak, m6A-express, and m6A-BERT have enabled the field to move from m6A detection to mechanistic interpretation, uncovering critical roles of m6A regulation in tumorigenesis and viral infection, particularly in the context of Kaposi's sarcoma-associated herpesvirus biology.
