School of Medicine


Showing 51-60 of 259 Results

  • Sharon Markham Geaghan

    Sharon Markham Geaghan

    Associate Professor of Pathology at the Stanford University Medical Center, Emerita

    Current Research and Scholarly InterestsPediatric Hematopathology, Pediatric Laboratory Medicine and Pathology

  • Pascal Geldsetzer

    Pascal Geldsetzer

    Assistant Professor of Medicine (Primary Care and Population Health) and, by courtesy, of Epidemiology and Population Health

    BioPascal Geldsetzer is an Assistant Professor of Medicine in the Division of Primary Care and Population Health and, by courtesy, in the Department of Epidemiology and Population Health. He is also affiliated with the Phil & Penny Knight Initiative for Brain Resilience at the Wu Tsai Neurosciences Institute, Department of Biomedical Data Science, Department of Health Policy, and the Stanford Center for Population Health Sciences.

    His research focuses on identifying and evaluating the most effective interventions for improving health at older ages. In addition to leading several randomized trials, his methodological emphasis lies on the use of natural experiments to ascertain causal effects in large observational datasets, particularly in electronic health record data. He has won an NIH New Innovator Award (in 2022), a Chan Zuckerberg Biohub investigatorship (in 2022), and three NIH R01 grants as Principal Investigator (in 2023 and 2024). In 2026, he was named one of the 100 most influential people in health and medicine globally by TIME Magazine.

  • Linda N. Geng, MD, PhD

    Linda N. Geng, MD, PhD

    Clinical Associate Professor, Medicine - Primary Care and Population Health

    Current Research and Scholarly InterestsMy scholarly focus is on puzzling and complex conditions. Our work aims to improve patients' diagnostic journeys, characterize poorly understood diseases, discover biological mechanisms, find treatments, improve care models, and reach communities in need.

    With the COVID pandemic, the puzzling and complex illness of Long COVID or post-acute COVID-19 syndrome (PACS) emerged. Together with a multidisciplinary group of physicians and researchers, we launched a program here at Stanford to advance the care and understanding of Long COVID. Our goal is to better understand the natural history, clinical symptomatology, immunological response, risk factors, and subtypes of Long COVID. We are also actively assessing treatment strategies for Long COVID and developing care pathways and tools for clinicians to help their patients with this and other related infection-associated chronic illnesses.

  • Grace Gengoux, PhD, BCBA-D

    Grace Gengoux, PhD, BCBA-D

    Clinical Professor, Psychiatry and Behavioral Sciences - Child & Adolescent Psychiatry and Child Development

    Current Research and Scholarly InterestsDr. Grace Gengoux is Director of the Autism Intervention Clinic and leads an autism intervention research program focused on developing and evaluating promising behavioral and developmental treatments for Autism Spectrum Disorder (ASD).

    Dr. Gengoux is also Associate Chair for Faculty Engagement & Well-being and Department Well-being Director in the Department of Psychiatry and Behavioral Sciences, leading the department's Standing Well-being Advisory Committee.

  • Mark Genovese

    Mark Genovese

    James W. Raitt M.D. Professor, Emeritus

    Current Research and Scholarly InterestsClinical trials and interventions in the rheumatic diseases including Rheumatoid Arthritis,Systemic Lupus Erythematosus, Systemic Sclerosis, Osteoarthritis.

  • Michael Gensheimer

    Michael Gensheimer

    Clinical Associate Professor, Radiation Oncology - Radiation Therapy

    Current Research and Scholarly InterestsIn addition to my clinical research in head and neck and lung cancer, I work on the application of computer science and machine learning to cancer research. I develop tools for analyzing large datasets to improve outcomes and safety of cancer treatment. I developed a machine learning prognostic model using data from around 13,000 patients with metastatic cancer which performs better than traditional models and physicians [PubMed ID 33313792]. We recently completed a prospective randomized study in thousands of patients in which the model was used to help improve advance care planning conversations.

    I also work on the methods underpinning observational and predictive modeling research. My open source nnet-survival software that allows use of neural networks for survival modeling has been used by researchers internationally. In collaboration with the Stanford Research Informatics Center, I examined how electronic medical record (EMR) survival outcome data compares to gold-standard data from a cancer registry [PubMed ID 35802836]. The EMR data captured less than 50% of deaths, a finding that affects many studies being published that use EMR outcomes data.