From diabetes to Covid-19, Better World showcases MIT research in action

Massachusetts Institute of Technology

“MIT’s work to understand and improve human health spans decades and covers the Institute,” said W. Eric L. Grimson PhD ’80, at MIT Better World (Health), a virtual gathering in February. “More than a third of the faculty representing every department at MIT engage in research directly related to health science and innovation.” Grimson, who is MIT’s chancellor for academic advancement and the Bernard M. Gordon Professor of Medical Engineering, spoke of the many achievements of Institute scholars in the human health arena: “Serving as the hub of the densest innovation cluster in the world, MIT is nimble and inventive, particularly when it comes to the life sciences.”

MIT alumni and friends from around the globe were invited to attend the online event, which featured presentations from Institute leaders, faculty, and alumni about human health-related research at the Institute. With more than 1,000 participants from 27 countries, the evening began with video greetings from nearly a dozen alumni working in a range of health-care roles all over the world. Their graduation years spanned five decades, from 1967 to 2019.

Innovations in Human Health Main Session and Q&A

Grimson then turned the spotlight over to the presenting speakers: Daniel P. Huttenlocher SM ’84 PhD ’88, dean of the MIT Stephen A. Schwarzman College of Computing and Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science; Mariana Arcaya MCP ’08, associate professor of urban planning and public health; and Steven Truong ’20, a Marshall Scholar studying computational biology at the University of Cambridge in England.

Huttenlocher spoke about the role of artificial intelligence in health research. Last year, he said, faculty at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health identified a new antibiotic candidate capable of killing drug-resistant bacteria. “In the search for new antibiotics, there are so many possibilities that it’s not practical to try even a small fraction of them,” he explained. “This is where machine learning comes in.”

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