Dr. David Sontag is Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and von Helmholtz Professor in the Institute for Medical Engineering and Science (IMES). He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT and co-founder and CEO at Layer Health. Dr. Sontag's research interests are in machine learning and artificial intelligence. As part of IMES, he leads a research group that aims to transform healthcare through the use of machine learning. Dr. Sontag joined MIT in 2017 from New York University, where he was Assistant Professor in Computer Science and Data Science from 2011 to 2016, and before this he was a postdoctoral researcher at Microsoft Research New England from 2010 to 2011. Dr. Sontag received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NeurIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award. Dr. Sontag received a B.A. from the University of California, Berkeley.

Isaac “Zak” Kohane, MD, PhD, is the inaugural chair of Harvard Medical School’s Department of Biomedical Informatics, whose mission is to develop the methods, tools, and infrastructure required for a new generation of scientists and care providers to move biomedicine rapidly forward by taking advantage of the insight and precision offered by big data. Kohane develops and applies computational techniques to address disease at multiple scales, from whole health care systems to the functional genomics of neurodevelopment.

He also has worked on AI applications in medicine since the 1990’s, including automated ventilator control, pediatric growth monitoring, detection of domestic abuse, diagnosing autism from multimodal data and most recently assisting clinicians using whole genome sequence and clinical histories to diagnose rare or unknown disease patients.

His most urgent question is how to enable doctors to be most effective and enjoy their profession when they enter into a substantial symbiosis with machine intelligence. He is a member of the National Academy of Medicine, the American Society for Clinical Investigation and the American College of Medical Informatics.