Bios
Uri Shalit
Uri Shalit is a senior lecturer (assistant professor) at the Technion - Israel Institute of Technology, Faculty of Industrial Engineering and Management, in the areas of statistics and information systems. Uri's research is currently focused on three subjects: The first is applying machine learning to the field of healthcare, especially in terms of providing physicians with decision support tools based on big health data. In this capacity he has worked with Clalit Health Services, Rambam Medical Center, and the Israeli Ministry of Health. The second subject is the intersection of machine learning and causal inference, especially the problem of learning individual-level causal effects. Finally, is interested in bringing ideas from causal inference into the field of machine learning, focusing on problems in robust learning, transfer learning and interpretability.
Previously, Uri was a postdoctoral researcher in Prof. David Sontag’s Clinical Machine Learning Lab in NYU and then MIT. He completed his PhD studies at the Center for Neural Computation at The Hebrew University of Jerusalem, under the guidance of Prof. Gal Chechik and Prof. Daphna Weinshall. From 2011 to 2014 he was a recipient of Google's European Fellowship in Machine Learning.
Shalmali Joshi
Shalmali Joshi is a Postdoctoral Fellow at the Center for Research on Computation and Society at Harvard University. Previously, she was a Postdoctoral Fellow at the Vector Institute. She received her Ph.D. from the University of Texas at Austin (UT Austin). Shalmali's research expertise is on developing reliable Machine Learning (ML) methods for clinical healthcare. She has contributed to ML research in explainability, algorithmic recourse, probabilistic modeling and causal inference for clinical decision-making. She has also contributed to interdisciplinary venues on ethical challenges of deploying ML tools in clinical healthcare.
Sendhil Mullainathan
Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth. His latest research is on computational medicine—applying machine learning and other data science tools to produce biomedical insights. In past work he has combined insights from behavioral science with empirical methods—experiments, causal inference tools, and machine learning—to study social problems such as discrimination and poverty. He currently teaches a course on Artificial Intelligence.
Outside of research, he co-founded a non-profit to apply behavioral science (ideas42), a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), has worked in government in various roles, and currently serves on the board of the MacArthur Foundation board. He is also a regular contributor to the New York Times.
Aisha Walcott-Bryant
Aisha is a Senior Technical Staff Member (STSM) and research manager at IBM Research Africa - Nairobi, Kenya. She leads a team of phenomenal, brilliant researchers and engineers that use AI, Cloud, and other technologies to advance the state-of-the art in the Future of Health and Climate while addressing business and societal needs. She has a strong interest in developing AI tools for Global Health (see our recent work on COVID-19 interventions), and working across sectors to create innovative, sustainable AI solutions to help transform emerging economies. She is also program co-chair for ICRA'22. Aisha earned her PhD in the Electrical Engineering and Computer Science Department at MIT in robotics, as a member of the Computer Science and Artificial Intelligence Lab (CSAIL).
Yoshua Bengio
Yoshua Bengio is Full Professor in the Department of Computer Science and Operations Research at Université de Montreal, as well as the Founder and Scientific Director of Mila and the Scientific Director of IVADO. Considered one of the world’s leaders in artificial intelligence and deep learning, he is the recipient of the 2018 A.M. Turing Award with Geoff Hinton and Yann LeCun, known as the Nobel prize of computing. He is a Fellow of both the Royal Society of London and Canada, an Officer of the Order of Canada, and a Canada CIFAR AI Chair.
Karandeep Singh
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 lab, which focuses on using machine learning and biomedical informatics methods to understand and improve health at scale. He chairs the Michigan Medicine Clinical Intelligence Committee, which oversees the implementation of machine learning models across the health system. 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.
Xiao Liu
Dr Xiao Liu is an ophthalmologist and a post doctoral researcher at the University of Birmingham and University Hospitals Birmingham NHS Foundation Trust. She is interested in evidence standards for AI in healthcare, to ensure AI innovations can safely and effectively improve patient care. Xiao co-led the development of SPIRIT-AI and CONSORT-AI, the first international reporting standards for clinical trials of AI interventions, and is working with other AI reporting standards in development, including STARD-AI, DECIDE-AI and TRIPOD-AI. She also works with regulatory and commissioning bodies, including MHRA, NICE, the UK National Screening Committee and the WHO/ITU AI4H focus groups, on their approaches to evaluating AI in healthcare.
Rory Sayres
Rory Sayres is a researcher in the Google Health AI group. He studies decision making processes related to the development and deployment of medical AI technology. This includes understanding how people make medical decisions; how AI explanations may impact decision making; and how human processes impact the labels used to train and validate AI models. His background includes neuroscience and human/computer interaction.