This week four CSAIL faculty were named among the top 100 global leaders in artificial intelligence for health, according to a new report developed by a top technology think-tank.
Deep Knowledge Analytics’s “Top 100 AI Leaders in Drug Discovery and Advanced Healthcare” (PDF) looked at scientists, clinicians and technologists across academia, pharma, and AI companies. Among the honorees were CSAIL principal investigators and MIT professors Regina Barzilay, Tommi Jaakkola, Manolis Kellis and Peter Szolovits.
Barzilay co-leads MIT CSAIL’s Natural Language Processing Group. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology. Jointly with MGH she is developing algorithms that can learn to improve models of disease progression, prevent over-treatment, and detect cancer earlier and more accurately. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, and the MacArthur “genius grant” fellowship.
Jaakkola co-leads the Natural Language Processing Group with Barzilay. His research interests include many aspects of machine learning, statistical inference and estimation, and analysis and development of algorithms for various modern estimation problems such as those involving predominantly incomplete data sources. His applied research focuses on problems in natural language processing, computational chemistry, as well as computational functional genomics.
Kellis directs MIT’s Computational Biology Group and is a member of both CSAIL and the Broad Institute. His research spans disease genetics, epigenomics, gene circuitry, and comparative genomics, and he has helped direct several large-scale genomics projects, including Roadmap Epigenomics, ENCODE, and Genotype Tissue-Expression (GTEx) project. He received the US Presidential Early Career Award in Science and Engineering (PECASE) by US President Barack Obama.
Szolovits heads up MIT CSAIL’s Clinical Decision-Making Group. His research centers on the application of AI methods to problems of medical decision making, predictive modeling, and system design for healthcare institutions. He has worked on problems of diagnosis, therapy planning and medical monitoring, computational aspects of genetic counseling, controlled sharing of health information, and privacy and confidentiality issues in medical record systems.