Captivated as a child by video games and puzzles, Marzyeh Ghassemi was also fascinated at an early age in health. Luckily, she found a path where she could combine the two interests.
"Although I had considered a career in health care, the pull of computer science and engineering was stronger," says Ghassemi, an associate professor in MIT's Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science (IMES) and principal investigator at the Laboratory for Information and Decision Systems (LIDS). "When I found that computer science broadly, and AI/ML specifically, could be applied to health care, it was a convergence of interests."
Today, Ghassemi and her Healthy ML research group at LIDS work on the deep study of how machine learning (ML) can be made more robust, and be subsequently applied to improve safety and equity in health.
Growing up in Texas and New Mexico in an engineering-oriented Iranian-American family, Ghassemi had role models to follow into a STEM career. While she loved puzzle-based video games - "Solving puzzles to unlock other levels or progress further was a very attractive challenge" - her mother also engaged her in more advanced math early on, enticing her toward seeing math as more than arithmetic.
"Adding or multiplying are basic skills emphasized for good reason, but the focus can obscure the idea that much of higher-level math and science are more about logic and puzzles," Ghassemi says. "Because of my mom's encouragement, I knew there were fun things ahead."
Ghassemi says that in addition to her mother, many others supported her intellectual development. As she earned her undergraduate degree at New Mexico State University, the director of the Honors College and a former Marshall Scholar - Jason Ackelson, now a senior advisor to the U.S. Department of Homeland Security - helped her to apply for a Marshall Scholarship that took her to Oxford University, where she earned a master's degree in 2011 and first became interested in the new and rapidly evolving field of machine learning. During her PhD work at MIT, Ghassemi says she received support "from professors and peers alike," adding, "That environment of openness and acceptance is something I try to replicate for my students."
While working on her PhD, Ghassemi also encountered her first clue that biases in health data can hide in machine learning models.
