School of Medicine


Showing 131-140 of 178 Results

  • Yuan James Rao, MD

    Yuan James Rao, MD

    Associate Professor of Radiation Oncology (Radiation Therapy)

    BioDr. Yuan James Rao is a board-certified radiation oncologist with Stanford Health Care. He is also an associate professor of radiation oncology and the associate director of proton therapy in the Department of Radiation Oncology, Division of Radiation Therapy at Stanford University School of Medicine.

    As a radiation oncologist, Dr. Rao treats chest (thoracic) and head and neck cancers. He specializes in using proton therapy, a type of high-energy radiation therapy that precisely targets cancer cells while sparing surrounding tissue. The proton therapies he uses include 3D conformal radiation therapy, intensity modulated radiation therapy, and stereotactic body radiation therapy. He also uses brachytherapy, which treats cancer by placing radiation sources inside or very close to a tumor.

    Dr. Rao’s research interests include the use of proton therapy in treating various cancers. He has also studied the role of machine learning and advanced imaging techniques to improve radiation treatments. In addition, Dr. Rao has investigated ways to integrate immunotherapy into radiation treatment regimens.

    Dr. Rao has published his work in and served as an ad hoc reviewer for several peer-reviewed journals, including Nature Cancer, Frontiers in Oncology, Advances in Radiation Oncology, and PLOS One. In addition, he has co-written chapters in books including Perez & Brady’s Principles and Practice of Radiation Oncology and Pocket Guide to Radiation Oncology. He has presented his work nationally and internationally, including at meetings of the American Brachytherapy Society (ABS), American Society for Radiation Oncology (ASTRO), and European Society for Radiation Oncology.

    Dr. Rao is a member of the ABS and ASTRO.

  • Jason B. Ross, MD, PhD

    Jason B. Ross, MD, PhD

    Assistant Professor of Radiation Oncology (Radiation Therapy)

    Current Research and Scholarly InterestsMy laboratory studies studying normal, dysfunctional, and malignant stem cells in the context of aging, cancer, and cancer therapies.

  • Mrinmoy Sanyal

    Mrinmoy Sanyal

    Casual - Non-Exempt, Radiation Oncology - Radiation Therapy

    BioMrinmoy Sanyal obtained his undergraduate and master's degree in Human Physiology at the University of Calcutta. He did his Ph.D. in Biochemistry at All India Institute of Medical Sciences, New Delhi, working on reproductive immunology, with the focus on trophoblast invasion and differentiation and their role in human blastocyst implantation. Then, he moved to Stanford University for a postdoctoral fellowship on the role of transcription factor Pbx1, a leukemia proto-oncogene, on B cell development. Currently, he is Research Scientist at Department of Biochemistry, Stanford University. His work covers various topics, including B cell responses to viral infection and vaccination, human primary immunodeficiency, and biology of lymphocyte development and function and to elucidate etiology of immunological disorders.

  • Mohammad Shahrokh Esfahani

    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.