School of Medicine
Showing 1-9 of 9 Results
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Daniel Aaron Gerber, MD
Clinical Assistant Professor, Medicine - Cardiovascular Medicine
BioDr. Gerber is a critical care cardiologist and co-director of Stanford's Cardiac ICU. He has dual subspecialty training in cardiovascular and critical care medicine and additional board certification in echocardiography. He completed his residency in internal medicine, fellowship in cardiovascular medicine, and an additional fellowship in critical care medicine at Stanford University and joined as faculty in 2021 as a Clinical Assistant Professor in the Department of Medicine’s Division of Cardiovascular Medicine.
Dr. Gerber manages the full spectrum of heart and vascular conditions with a focus on critically ill patients with life-threatening cardiovascular disease. He is active in medical education, teaching introductory echocardiography to Stanford medical students and residents, co-directing the Stanford Critical Care Medicine Critical Care Ultrasound Program, and lecturing nationally on critical care echocardiography and point-of-care ultrasonography at the Society of Critical Care Medicine’s annual congress. Finally, Dr. Gerber’s research interests focus on optimizing cardiac intensive care, including working with the Critical Care Cardiology Trials Network (CCCTN) - a national network of tertiary cardiac ICUs coordinated by the TIMI Study Group - and studying acute mechanical circulatory support techniques to improve patient outcomes and care processes. -
Joshua Gillard
Postdoctoral Scholar, Cardiovascular Medicine
BioDr. Josh Gillard is a Canadian biomedical data scientist with experience in bioinformatics, machine learning, and immunology. After completing a BSc and a MSc in Experimental Medicine at McGill university, he relocated to the Netherlands for his PhD at Radboud University in Nijmegen. During his PhD, he gained experience analyzing and interpreting complex immunological data (bulk and single-cell transcriptomics, high-dimensional cytometry, proteomics data) derived from human observational or intervention studies (vaccination and experimental human infection) in order to discover molecular and cellular correlates of clinically important endpoints such as disease severity, symptom progression, and antibody responses. In 2022, Josh relocated to Stanford to join the Gaudilliere lab to develop and apply multi-omic data integration and machine learning techniques, establishing that early gestational immune dysregulation can predict preterm birth. Since 2024, in the Ashley lab, Josh is focused on the use of deep learning and transformer models to identify novel splice isoforms of hypertrophic cardiomyopathy using whole genome sequencing data.
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Sneha Goenka
Postdoctoral Scholar, Cardiovascular Medicine
Ph.D. Student in Electrical Engineering, admitted Autumn 2017
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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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.
