Yoshua Bengio / University of Montreal
Yoshua Bengio is Professor in the Computer Science and Operations Research departments at U. Montreal, founder and scientific director of Mila and of IVADO. He is a Fellow of the Royal Society of London and of the Royal Society of Canada, has received a Canada Research Chair and a Canada CIFAR AI Chair and is a recipient of the 2018 Turing Award for pioneering deep learning, is an officer of the Order of Canada, a member of the NeurIPS advisory board, co-founder and member of the board of the ICLR conference, and program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncovering the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.
Elaine Nsoesie / Boston University
Dr. Nsoesie is an Assistant Professor of Global Health at Boston University (BU) School of Public Health. She is also a BU Data Science Faculty Fellow as part of the BU Data Science Initiative at the Hariri Institute for Computing and a Data and Innovation Fellow at The Directorate of Science, Technology and Innovation (DSTI) in the Office of the President in Sierra Leone. Dr. Nsoesie applies data science methodologies to global health problems, using digital data and technology to improve health, particularly in the realm of surveillance of chronic and infectious diseases. She has worked with local public health departments in the United States and international organizations. She completed her postdoctoral studies at Harvard Medical School, and her PhD in Computational Epidemiology from the Genetics, Bioinformatics and Computational Biology program at Virginia Tech. She also has an MS in Statistics and a BS in Mathematics. She is the founder of Rethé – an initiative focused on providing scientific writing tools and resources to student communities in Africa in order to increase representation in scientific publications. She has written for NPR, The Conversation, Public Health Post and Quartz. Dr. Nsoesie was born and raised in Cameroon.
Sherri Rose / Harvard Medical School
Sherri Rose, Ph.D. is an Associate Professor of Health Care Policy at Harvard Medical School and Co-Director of the Health Policy Data Science Lab. Her research in health policy focuses on risk adjustment, comparative effectiveness, and health program evaluation. Dr. Rose coauthored the first book on machine learning for causal inference and has published work across fields, including in Biometrics, JASA, PMLR,Journal of Health Economics, and NEJM. She currently serves as co-editor of the journal Biostatistics and is Chair-Elect of the American Statistical Association’s Biometrics Section. Her honors include the ISPOR Bernie J. O’Brien New Investigator Award for exceptional early career work in health economics and outcomes research and an NIH Director’s New Innovator Award to develop machine learning estimators for generalizability in health policy.
Ruslan Salakhutdinov / Carnegie Mellon University
Dr. Ruslan Salakhutdinov is a UPMC professor of Computer Science at Carnegie Mellon University. He has served as an area chair for NIPS, ICML, CVPR, and ICLR. He holds a PhD from University of Toronto and completed postdoctoral training at Massachusetts Institute of Technology.
Nigam Shah / Stanford University
Dr. Nigam Shah is Associate Professor of Medicine (Biomedical Informatics) at Stanford University, and serves as the Associate CIO for Data Science for Stanford Health Care. Dr. Shah’s research focuses on combining machine learning and prior knowledge in medical ontologies to enable the learning health system. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and is 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.