Invited Talk on Research and Top Recent Papers from 2020-2022
Bio: Suchi Saria, PhD, holds the John C. Malone endowed chair and is the Director of the Machine Learning, AI and Healthcare Lab at Johns Hopkins. She is also is the Founder and CEO of Bayesian Health. Her research has pioneered the development of next generation diagnostic and treatment planning tools that use statistical machine learning methods to individualize care. She has written several of the seminal papers in the field of ML and its use for improving patient care and has given over 300 invited keynotes and talks to organizations including the NAM, NAS, and NIH. Dr. Saria has served as an advisor to multiple Fortune 500 companies and her work has been funded by leading organizations including the NIH, FDA, NSF, DARPA and CDC.Dr. Saria’s has been featured by the Atlantic, Smithsonian Magazine, Bloomberg News, Wall Street Journal, and PBS NOVA to name a few. She has won several awards for excellence in AI and care delivery. For example, for her academic work, she’s been recognized as IEEE’s “AI’s 10 to Watch”, Sloan Fellow, MIT Tech Review’s “35 Under 35”, National Academy of Medicine’s list of “Emerging Leaders in Health and Medicine”, and DARPA’s Faculty Award. For her work in industry bringing AI to healthcare, she’s been recognized as World Economic Forum’s 100 Brilliant Minds Under 40, Rock Health’s “Top 50 in Digital Health”, Modern Healthcare’s Top 25 Innovators, The Armstrong Award for Excellence in Quality and Safety and Society of Critical Care Medicine’s Annual Scientific Award.
Invited Talk on Recent Deployments and Real-world Impact
Bio: Karandeep Singh, MD, MMSc, is an Assistant Professor of Learning Health Sciences, Internal Medicine, Urology, and Information at the University of Michigan. He directs the Machine Learning for Learning Health Systems (ML4LHS) Lab, which focuses on translational issues related to the implementation of machine learning (ML) models within health systems. He serves as an Associate Chief Medical Information Officer for Artificial Intelligence for Michigan Medicine and is the Associate Director for Implementation for U-M Precision Health, a Presidential Initiative focused on bringing research discoveries to the bedside, with a focus on prediction models and genomics data. He chairs the Michigan Medicine Clinical Intelligence Committee, which oversees the governance of machine learning models across the health system. He teaches a health data science course for graduate and doctoral students, and provides clinical care for people with kidney disease. He completed his internal medicine residency at UCLA Medical Center, where he served as chief resident, and a nephrology fellowship in the combined Brigham and Women’s Hospital/Massachusetts General Hospital program in Boston, MA. He completed his medical education at the University of Michigan Medical School and holds a master’s degree in medical sciences in Biomedical Informatics from Harvard Medical School. He is board certified in internal medicine, nephrology, and clinical informatics.
Invited Talk on Under-explored Research Challenges and Opportunities
Bio: Dr. Nigam Shah is Professor of Medicine at Stanford University, and Chief Data Scientist for Stanford Health Care. His research group analyzes multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system. At Stanford Healthcare, he leads artificial intelligence and data science efforts for advancing the scientific understanding of disease, improving the practice of clinical medicine and orchestrating the delivery of health care. Dr. Shah is an inventor on eight patents and patent applications, has authored over 200 scientific publications and has co-founded three companies. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and was inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.