School of Medicine
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Mohammad Shahrokh Esfahani
Assistant Professor of Radiation Oncology (Radiation and Cancer Biology)
BioWith a primary focus on high-dimensional data, I have significant expertise in developing machine learning tools. Much of my work involves constructing Bayesian models, which effectively convert 'prior knowledge', either inherent in the dataset or obtained from external sources, into mathematical terms—more specifically, prior probabilities.
My recent research efforts have centered on the analysis of genetic and epigenetic signals within cell-free DNA assays. This interest in epigenetics led to the development of a pioneering technique known as EPIC-seq, which has broadened our understanding of this complex field.
It's notable that traditional computational methods in cancer genomics often fall short when confronted with an exceedingly low signal-to-noise ratio—a common scenario in cfDNA analyses. As such, there's an emerging need to devise innovative, robust methods capable of overcoming this limitation—a research area that I'm deeply committed to and actively engaged in. -
Junming Seraphina Shi
Postdoctoral Scholar, Radiation Biology
BioI am a postdoctoral fellow at Stanford University, jointly mentored by Dr. Mohammad Shahrokh Esfahani and Dr. Md Tauhidul Islam. My research focuses on developing robust statistical machine learning methods for noninvasive, cost-effective cancer diagnostics, with applications in early detection, treatment monitoring, and precision oncology.
I received my Ph.D. from UC Berkeley, where my dissertation centered on advancing biostatistical machine learning approaches for complex biomedical challenges. My work addressed causal inference for continuous treatments, bias and measurement patterns in ICU electronic health records, and deep learning–based biclustering and prediction of cancer-drug responses. Across these projects, I developed interpretable and scalable tools for analyzing high-dimensional, multimodal clinical data.
At Stanford, I continue to build novel statistical learning frameworks tailored to real-world clinical needs—particularly through the analysis of liquid biopsy (cell-free DNA) and cancer imaging data. My current work aims to improve cancer detection and monitoring, with a focus on noninvasive, accessible, and clinically meaningful solutions to pressing challenges in oncology. I enjoy interdisciplinary collaborations and working across fields to drive innovation in biomedical research. Deeply committed to cancer research, I aim to bridge rigorous computational methodology with patient-centered impact by designing tools that are scalable, equitable, and translational.
