School of Medicine
Showing 31-40 of 90 Results
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Jenelle Asha Jindal
Affiliate, Med/BMIR
BioDr. Jenelle Jindal is a physician who believes in bringing well designed technology into healthcare. She has spent years in the healthcare system practicing medicine, as well as in hospital leadership roles and in government during the pandemic. She is a graduate of Stanford University, Yale School of Medicine, and completed residency and fellowship at the Harvard hospitals of Mass General and Brigham & Womens.
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Detailed Bio
Dr. Jenelle Jindal has experience as an operator within the healthcare system, serving as medical director at El Camino Hospital in Silicon Valley launching telestroke and 24/7/365 thrombectomy care. Subsequently the hospital was recognized by the Joint Commission with a new higher level of Thrombectomy Capable Certification. She also completed a tour of duty helping in the Emergency Operations Center of Santa Clara County during the COVID-19 pandemic assisting with antigen testing deployment and increasing vaccination uptake.
She is also an experienced neurologist, caring for thousands of patients as a practicing physician. Her clinical focus has included treating strokes, brain hemorrhage, epilepsy, and neurodegenerative disease in the emergency room, ICU, and hospital wards. She was founder and CEO running a private medical practice for nearly 7 years and served as a Medical Advisory Board member of the Pacific Stroke Association.
LinkedIn: www.linkedin.com/in/jenellejindal/ -
Teri Klein
Professor (Research) of Biomedical Data Science, of Medicine (BMIR) and, by courtesy, of Genetics
Current Research and Scholarly InterestsCo-founder, Pacific Symposium on Biocomputing
NIEHS, Site Visit Reviewer
NIH, Study Section Reviewer -
Curtis Langlotz
Professor of Radiology (Thoracic Imaging), of Medicine (Biomedical Informatics Research), of Biomedical Data Science and Senior Fellow at the Stanford Institute for HAI
Current Research and Scholarly InterestsI am interested in the use of deep neural networks and other machine learning technologies to help radiologists detect disease and eliminate diagnostic errors. My laboratory is developing deep neural networks that detect and classify disease on medical images. We also develop natural language processing methods that use the narrative radiology report to create large annotated image training sets for supervised machine learning experiments.
