School of Medicine
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Mohammad Shahrokh Esfahani
Assistant Professor of Radiation Oncology (Radiation and Cancer Biology)
BioI lead a computational oncology laboratory that develops machine learning and statistical methods for high-dimensional genomics, with particular expertise in Bayesian and uncertainty-aware modeling to integrate prior biological knowledge with large-scale datasets.
Our research centers on liquid biopsy analytics—especially cell-free DNA (cfDNA)—to noninvasively quantify genetic and epigenetic states relevant to cancer detection, monitoring, and tumor evolution. We developed EPIC-seq, a fragmentomics-based method that uses cfDNA fragmentation patterns to infer regulatory activity and gene expression programs, providing a scalable framework for epigenetic profiling from blood.
A core methodological focus of the lab is enabling reliable inference in extremely low signal-to-noise settings that are typical of cfDNA and early-stage disease. We build robust, interpretable models and benchmarking frameworks that support clinical translation, with the long-term aim of democratizing access to sensitive, minimally invasive cancer diagnostics.
