Dr. Amar Gupta rejoined MIT in 2015 to lead and conduct research activities at the Computer Science and Artificial Intelligence Laboratory (CSAIL), the Department of Electrical Engineering and Computer Science (EECS), and the Institute for Medical Engineering & Science (IMES). He developed the new course, rated highly by students, on "Telemedicine and Telehealth for Global Health" which includes distinguished experts from different parts of the US and abroad.
Earlier, he worked at the MIT Sloan School for 25 years. Between 2004 and 2012, he served in multiple capacities at the University of Arizona, including Thomas R. Brown Endowed Professor of Management and Technology, and tenured full Professor of Entrepreneurship. For parts of this period, he also served as Senior Director for Research and Business Development, Professor of Computer Science, Professor of Latin American Studies, Professor of Law, and Professor of Pharmacy. He was selected as The Best Teacher in 2011 by the students of the Eller College of Management by an unprecedented margin. Between 2012 and 2015, he served as Dean of Seidenberg School of Computer Science and Information Science, and during his tenure there was a significant increase in enrollment, and the online degree programs were ranked #1 in US News and World Report. He has served as an advisor to various agencies of the United Nations and has published 12 books. His papers have been accepted in journals from diverse fields and he has played a significant role in nucleating several technologies that are in broad use today. He continues to do research in the areas of: 24-Hour Knowledge Factory; Geographically Distributed Teams; Telemedicine; Telehealth; and Global Health. His latest endeavor is a multi-year project on Telemedicine funded in part by a large multinational company. Dr. Gupta holds an undergraduate in Electrical Engineering, a graduate degree in Management (from MIT Sloan School) and a doctorate in Computer Science.
MIT’s "FairRAG" architecture is fast & privacy-preserving. It uses differentially private synthetic data & retrieval-augmented generation — & outperforms traditional ML & LLMs
