School of Medicine
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Ananya Pradhan
Affiliate, Radiation Oncology - Radiation Biology
BioMy research sits at the intersection of deep learning, computational analysis, and understanding high-dimensional biological data to create a positive impact for patients. I am deeply fascinated by computer science and have experience working with generative networks and deep neural networks.
Currently, I am working with Dr. Seraphina Shi in the Esfahani Lab. My research focuses on analyzing epigenetic features of cfDNA fragments to develop deep learning architectures for early lung cancer detection. My prior independent research involved using variational auto-encoders and optical coherence tomography data to detect Alzheimer's disease at an early stage.
Outside of research, I enjoy student leadership and debate, and I’m always happy to have great conversations. I also love movies and spending time in nature. If any of these topics interest you, feel free to reach out! -
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.
