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Oct. 25, 2020

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Prof. Regina Barzilay has won a 1M$ award granted by the Association for the Advancement of Artificial Intelligence (AAAI) for her AI-based breakthroughs in early identification of breast cancer and discovering a new, powerful antibiotic. “Only world-renowned recognitions, such as the Association of Computing Machinery's A.M. Turing Award and the Nobel Prize, carry monetary rewards at the million-dollar level," says AAAI awards committee chair Yolanda Gil. “This award aims to be unique in recognizing the positive impact of artificial intelligence for humanity."

​Prof. Regina Barzilay completed both her BSc and MSc degrees in the department of Computer Science at Ben-Gurion University. She obtained her BSc. in 1993, and her MSc., under the supervision of Prof. Michael Elhadad, five years later. She studied for her PhD in Columbia University and joined the MIT faculty in 2003, where she now holds a professor position at the school of engineering and computer science.

Prof. Barzilay's personal life story directly influenced her scientific work. In 2014, she was diagnosed with breast cancer. Since then, her research has increasingly focused on computational healthcare in general and on using Machine Learning techniques for improving early medical diagnosis, treatment and disease prevention, in particular.

In another key scientific breakthrough made by a team of MIT researchers led by Prof. Barzily and Prof. Collins, a novel powerful antibiotic was discovered, using Machine Learning models, that succeeds in killing many disease-causing species of antibiotic-resilient bacteria. This is the first time in over 30 years that a completely new antibiotic molecule was discovered!

Prof. Barzilay is also the recipient of the prestigious MacArthur Fellowship (in 2017), known as the "Genius Grant". The $625,000 prize is awarded yearly to a few dozens of US citizens or residents, in any field, that have shown exceptional creativity and originality. 

Click here for the article published on MIT's news site


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Photo by Rachel Wu​


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