Stanford University
Showing 51-60 of 121 Results
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Sneha Goenka
Postdoctoral Scholar, Cardiovascular Medicine
Stanford Student Employee, Hoover InstitutionBioSneha Goenka is a Ph.D. candidate in the Electrical Engineering Department at Stanford University where she is advised by Prof. Mark Horowitz. Her research centers on designing efficient computer systems for advancing genomic pipelines for clinical and research applications, with a focus on improving speed and cost. She is a 2023 Forbes 30 Under 30 Honoree in the Science category, 2022 NVIDIA Graduate Fellow, and 2021 Cadence Women in Technology Scholar. She has a B.Tech. and M.Tech. (Microelectronics) in Electrical Engineering from the Indian Institute of Technology, Bombay where she received the Akshay Dhoke Memorial Award for the most outstanding student in the program.
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Ethan Goh, MD, MS
Postdoctoral Scholar, General Internal Medicine
BioDr. Goh's research focuses on AI in healthcare, digital health, and informatics. He has successfully led multi-site, grant-funded evaluation studies on Large Language Models applications within healthcare. Prior to Stanford, he was an Internal Medicine clinician, startup founder and technology consultant, working with partners like Google, OpenAI, Roche, Samsung, IDEO, and the NHS in the development, validation and commercialization of digital health products and AI technology. He holds an MD from Imperial College, London, and a Masters in Clinical Informatics and Management from Stanford University.
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Bruna Filipa Gomes Botelho Quintas
Postdoctoral Scholar, Cardiovascular Medicine
Current Research and Scholarly InterestsThe increasing availability of very large datasets, along with recent advances in deep learning based tools for automatic extraction of cardiac traits, has led to the discovery of further common variants associated with cardiac disease. However, the genetic underpinnings of valvular heart disease remains understudied. I am interested in developing deep learning techniques to automatically extract cardiac flow information to facilitate genome-wide association studies of cardiac flow traits.