Dr. Danh-Tai Hoang is a Research Scientist II at the Jim and Eleanor Randal Department of Surgery.

His research focuses on developing novel AI frameworks for personalized cancer medicine, with the goal of selecting the most effective treatment for each patient In particular, he led the development of: (i) DeepPT, a deep learning model that predicts RNA-seq gene expression from pathology slides (Hoang et al., Nature Cancer 2024); and (ii) DEPLOY, a deep learning model that predicts DNA methylation from pathology slides (Hoang et al., Nature Medicine 2024). More recently, he developed Path2Omics, an advanced framework building on DeepPT and DEPLOY. This study demonstrated that the gene expression inferred by Path2Omics closely matches experimentally measured expression for predicting patient survival and treatment response (Hoang et al., Cancer Research 2025), paving the way for precision oncology without the need for time-consuming and costly sequencing. Path2Omics has since been incorporated into subsequent studies to predict response to neoadjuvant therapies (BRIDGE; Cantore et al., Annals of Oncology, 2026) and immunotherapies (TIME_ACT; Mukherjee, et al., BioRxiv 2025).

Currently, Dr. Hoang is developing AI models to predict spatial proteomics and spatial transcriptomics at both the single-cell and spot levels directly from pathology slides. These efforts aim to identify spatially resolved biomarkers that improve the predictions of patient survival and treatment response while advancing precision oncology.

Prior to joining Cedars-Sinai, he served as a Staff Scientist at the Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH, USA), and a Research Fellow (Academic Staff Level B) at the Biological Data Science Institute (BDSI), Australian National University (ANU). He received a PhD degree in Theoretical Physics from CY Cergy Paris University (formerly Cergy-Pontoise University, France).