Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director of Mila and of IVADO, and Canada Research Chair in Statistical Learning Algorithms. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NIPS board and co-founder and general chair for the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains.

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.

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.

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.

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.