Regina Barzilay
Regina Barzilay / MIT Computer Science & Artificial Intelligence Lab
Regina Barzilay is a professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. She is an AI faculty lead for Jameel Clinic, an MIT center for Machine Learning in Health at MIT. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. In 2020, she was awarded AAAI Squirrel Award for Artificial Intelligence for the Benefit of Humanity. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University. Regina received her undergraduate from Ben Gurion University of the Negev, Israel.
Narges Razavian
Narges Razavian / New York University Langone Medical Center
Narges Razavian is an assistant professor at NYU Langone Health, Center for Healthcare Innovation and Delivery Sciences, and Predictive Analytics Unit. Her lab focuses on various applications of Machine Learning and AI for medicine with a clinical translation outlook, and they work with Medical Images, Clinical Notes, and Electronic Health Records. Before NYU Langone, she was a postdoc at CILVR lab at NYU Courant CS department. She received her PhD at CMU Computational Biology group.
Mark Sendak
Mark Sendak / Duke Institute for Health Innovation
Mark Sendak, MD, MPP is the Population Health & Data Science Lead at the Duke Institute for Health Innovation (DIHI), where he leads interdisciplinary teams of data scientists, clinicians, and machine learning experts to build technologies that solve real clinical problems. He has built tools to support Duke Health's Accountable Care Organization, COVID-19 Pandemic Response Network, and hospital network. Together with his team, he has integrated dozens of data-driven technologies into clinical operations and is a co-inventor of software to scale machine learning applications. He leads the DIHI Clinical Research & Innovation scholarship, which equips medical students with the business and data science skills required to lead health care innovation efforts. His work has been published in technical venues such as the Machine Learning for Healthcare Proceedings and Fairness, Accountability, and Transparency in Machine Learning Proceedings and clinical journals such as Plos Medicine, Nature Medicine and JAMA Open. He has served as an expert advisor to the American Medical Association, AARP, and National Academies of Medicine on matters related to machine learning, innovation, and policy. He obtained his MD and Masters of Public Policy at Duke University as a Dean's Tuition Scholar and his Bachelor's of Science in Mathematics from UCLA.
 Alan Karthikesalingam
Alan Karthikesalingam / Google Health - London
Dr. Alan Karthikesalingam is a surgeon-scientist who leads the healthcare machine learning research group at Google Health in London (and formerly for healthcare at DeepMind).

He led DeepMind and Google’s teams in four landmark studies in Nature and Nature Medicine focusing on AI for breast cancer screening with Cancer Research UK, AI for the recognition and prediction of blinding eye diseases with the world’s largest eye hospital (Moorfields) and medical records research with the Veterans Affairs developing AI early warning systems for common causes of patient deterioration, like acute kidney injury.

He is leading work on how machine learning approaches can best promote AI safety as the team takes forward its early research into products for clinical care. Alan continues to practice clinically and supervise PhD students as a lecturer in the vascular surgery department of Imperial College, London.
Maia Jacobs
Maia Jacobs / Northwestern University
Dr. Maia Jacobs is an assistant professor at Northwestern University in Computer Science and Preventive Medicine. Her research contributes to the fields of Computer Science, Human-Computer Interaction (HCI), and Health Informatics through the design and evaluation of novel computing approaches that provide individuals with timely, relevant, and actionable health information. Recent projects include the design and deployment of mobile tools to increase health information access in rural communities, evaluating the influence of AI interface design on expert decision making, and co-designing intelligent decision support tools with clinicians. Her research has been funded by the National Science Foundation, the National Cancer Institute, and the Harvard Data Science Institute and has resulted in the deployment of tools currently being used by healthcare systems and patients around the country. She completed her PhD in Human Centered Computing at Georgia Institute of Technology and was a postdoctoral fellow in the Center for Research on Computation and Society at Harvard University. Jacobs’ work was awarded the iSchools Doctoral Dissertation Award, the Georgia Institute of Technology College of Computing Dissertation Award, and was recognized in the 2016 report to the President of the United States from the President's Cancer Panel, which focused on improving cancer-related outcomes.
Tianxi Cai
Tianxi Cai / Harvard Medical School
Dr. Tianxi Cai is the John Rock Professor of Population and Translational Data Science jointly appointed in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health (HSPH) and the Department of Biomedical Informatics (DBMI), Harvard Medical School, where she directs the Translational Data Science Center for a learning healthcare system. Her recent research has been focusing on developing interpretable and robust statistical and machine learning methods for deriving precision medicine strategies and more broadly for mining large-scale biomedical data including electronic health records data.