Andrew H. Song, PHD
Department of Translational Molecular Pathology, Division of Pathology-Lab Medicine Div
About Dr. Andrew Song
Dr. Andrew Song is an Assistant Professor in the Department of Translational Molecular Pathology at UT MD Anderson cancer center. He is also an Adjunct Professor in Computer Science at Rice University and affiliate of the Institute for Data Science in Oncology.
The research group is dedicated to building next-generation AI tools for computational pathology, grounded in rigorous principles of statistical inference, with the overarching goal of deciphering multi-scale oncologic complexity. The lab’s research will focus on developing state-of-the-art AI models (foundation models, AI agents) capable of integrating and cross-analyzing diverse data modalities—such as tissue images, spatial transcriptomics, spatial proteomics, and clinical reports—across multiple dimensions of clinical data (2D, 3D, and 4D longitudinal datasets). By combining these innovations with advanced statistical approaches such as Bayesian inference and optimal transport, the lab aims to open new frontiers in computational pathology and precision oncology.
More about his research can be found at https://andrewhsong.com
Present Title & Affiliation
Primary Appointment
Assistant Professor, Translational Molecular Path, The University of Texas MD Anderson Cancer Center, Houston, Texas
Education & Training
Degree-Granting Education
| 2016 | MIT, Electrical Engineering and Computer Science, M.Eng |
| 2016 | MIT, Electrical Engineering and Computer Science, B.S |
| 2022 | MIT, Electrical Engineering and Computer Science, Ph.D |
Postgraduate Training
| 2022-2026 | Postdoctoral research fellow, Brigham and Women's Hospital, Boston, Massachusetts |
Selected Publications
Peer-Reviewed Articles
- Shao D, Runevic J, Chen RJ, Williamson DF, Kim A, Song AH, Mahmood F. Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning. ICLR, 2026. e-Pub 2026.
- Ding T, Wagner SJ, Song AH, Chen RJ, Lu MY, Zhang A, Vaidya AJ, Jaume G, Shaban M, Kim A, Williamson DF, Chen B, Almagro-Perez C, Doucet P, Sahai S, Chen C, Komura D, Kawabe A, Ishikawa S, Gerber G, Peng T, Le LP, Mahmood F. Multimodal Whole Slide Foundation Model for Pathology. Nature Medicine, 2025. e-Pub 2025.
- Jaume G, Doucet P, Song A, Lu MY, Pérez CA, Wagner S, Vaidya A, Chen R, Williamson D, Kim A, Mahmood F. Hest-1k: A dataset for spatial transcriptomics and histology image analysis. NeurIPS, 2024. e-Pub 2024.
- Song AH, Chen RJ, Jaume G, Vaidya AJ, Baras A, Mahmood F. Multimodal Prototyping for cancer survival prediction. ICML, 2024. e-Pub 2024.
- Song AH, Chen RJ, Ding T, Williamson DF, Jaume G, Mahmood F. Morphological prototyping for unsupervised slide representation learning in computational pathology. CVPR, 2024. e-Pub 2024.
- Song AH, Williams M, Williamson DF, Chow SS, Jaume G, Gao G, Zhang A, Chen B, Baras AS, Serafin R, Colling R, Downes MR, Farré X, Humphrey P, Verrill C, True LD, Parwani AV, Liu JT, Mahmood F. Analysis of 3D pathology samples using weakly supervised AI. Cell 187(10), 2024. e-Pub 2024.
- Vaidya A, Chen RJ, Williamson DF, Song AH, Jaume G, Yang Y, Hartvigsen T, Dyer EC, Lu MY, Lipkova J, Shaban M, Chen TY, Mahmood F. Demographic bias in misdiagnosis by computational pathology models. Nature Medicine, 2024. e-Pub 2024.
- Chen RJ, Ding T, Lu MY, Williamson DF, Jaume G, Song AH, Chen B, Zhang A, Shao D, Shaban M, Williams M, Oldenburg L, Weishaupt LL, Wang JJ, Vaidya A, Le LP, Gerber G, Sahai S, Williams W, Mahmood F. Towards a general-purpose foundation model for computational pathology. Nature Medicine, 2024. e-Pub 2024.
Review Articles
- Song AH, Jaume G, Williamson DF, Lu MY, Vaidya A, Miller TR, Mahmood F. Artificial intelligence for digital and computational pathology. Nature Reviews Bioengineering, 2023. e-Pub 2023.
Patient Reviews
CV information above last modified June 15, 2026