Stanford University
Showing 27,421-27,440 of 34,960 Results
-
Sumit Shah
Clinical Associate Professor, Medicine - Oncology
Clinical Associate Professor (By courtesy), UrologyBioDr. Sumit Shah is a medical oncologist specializing in the management of advanced urologic malignancies, including prostate, bladder, kidney, and testicular cancers. He is actively involved in clinical trials, with a particular focus on novel immunotherapy agents and biomarkers to select patients most likely to benefit from therapy. His academic interests also encompass digital health technologies and innovative healthcare delivery models. He has lectured internationally on the management of urologic cancers and currently serves on the National Cancer Center Network (NCCN) Panel for testicular cancer.
Dr. Shah graduated with distinction in biomedical engineering from Duke University, earned his MD from Stanford University, and completed a Master of Public Health at Harvard University. He trained in internal medicine at the University of California, San Francisco (UCSF), where he remained on faculty for one year before returning to Stanford for his fellowship in medical oncology. He now holds a faculty position in the Department of Medicine (Oncology) at Stanford.
In addition to his clinical and research roles, Dr. Shah serves as Medical Director of Digital Healthcare Integration and Director of Infusion Services at Stanford Hospital. He is also Assistant Dean of Academic Advising in the Stanford School of Medicine. -
Yash Shah
Ph.D. Student in Computer Science, admitted Autumn 2025
Current Research and Scholarly InterestsMy research interests lie in developing neuroconnectionist mechanistic models of the brain that deepen our understanding of neural computation and representations. I aim to explore how physiological and anatomical constraints shape cortical topography and, in turn, scaffold development. I am particularly intrigued by observing certain behaviors emerge from mechanistic models, even when the model was not optimized to do so.
-
Shagufta Shaheen
Clinical Assistant Professor, Medicine - Oncology
BioDr. Shaheen specializes in the gastrointestinal malignancies and she has expertise in treating neuroendocrine tumors (NETs). Following her fellowship in Hematology and Oncology, Dr Shaheen completed an advanced fellowship in Neuroendocrine tumors from Stanford University. The NET advanced fellowship is first of its kind in United State started under the leadership of Dr Pamela Kunz who is the founding Director of the Stanford Neuroendocrine Tumor Program established in 2015. After completing her advanced fellowship, Dr Shaheen joined Stanford Oncology division as Clinical Assistant Professor. Dr Shaheen is involved in further developing the neuroendocrine oncology program at Stanford which serves as a centre of excellence in the treatment of neuroendocrine tumors. Dr Shaheen is actively involved in clinical research and clinical trials. Dr Shaheen is also involved in taking care of patients admitted to the oncology service as well as resident and fellow teaching.
-
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.
