Captivated as a baby by video games and puzzles, Marzyeh Ghassemi was also fascinated at an early age in health. Luckily, she found a path where she could mix the 2 interests.
“Although I had considered a profession 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 on the Laboratory for Information and Decision Systems (LIDS). “After I found that computer science broadly, and AI/ML specifically, might 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) might be made more robust, and be subsequently applied to enhance safety and equity in health.
Growing up in Texas and Recent Mexico in an engineering-oriented Iranian-American family, Ghassemi had role models to follow right into a STEM profession. While she loved puzzle-based video games — “Solving puzzles to unlock other levels or progress further was a really attractive challenge” — her mother also engaged her in more advanced math early on, enticing her toward seeing math as greater than arithmetic.
“Adding or multiplying are basic skills emphasized for good reason, but the main focus can obscure the concept much of higher-level math and science are more about logic and puzzles,” Ghassemi says. “Due to my mom’s encouragement, I knew there have been fun things ahead.”
Ghassemi says that along with her mother, many others supported her mental development. As she earned her undergraduate degree at Recent 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 use for a Marshall Scholarship that took her to Oxford University, where she earned a master’s degree in 2011 and first became fascinated with the brand 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 copy 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.
She had trained models to predict outcomes using health data, “and the mindset on the time was to make use of all available data. In neural networks for images, we had seen that the proper features can be learned for good performance, eliminating the necessity to hand-engineer specific features.”
During a gathering with Leo Celi, principal research scientist on the MIT Laboratory for Computational Physiology and IMES and a member of Ghassemi’s thesis committee, Celi asked if Ghassemi had checked how well the models performed on patients of various genders, insurance types, and self-reported races.
Ghassemi did check, and there have been gaps. “We now have almost a decade of labor showing that these model gaps are hard to handle — they stem from existing biases in health data and default technical practices. Unless you consider carefully about them, models will naively reproduce and extend biases,” she says.
Ghassemi has been exploring such issues ever since.
Her favorite breakthrough within the work she has done got here about in several parts. First, she and her research group showed that learning models could recognize a patient’s race from medical images like chest X-rays, which radiologists are unable to do. The group then found that models optimized to perform well “on average” didn’t perform as well for girls and minorities. This past summer, her group combined these findings to show that the more a model learned to predict a patient’s race or gender from a medical image, the more serious its performance gap can be for subgroups in those demographics. Ghassemi and her team found that the issue might be mitigated if a model was trained to account for demographic differences, as a substitute of being focused on overall average performance — but this process needs to be performed at every site where a model is deployed.
“We’re emphasizing that models trained to optimize performance (balancing overall performance with lowest fairness gap) in a single hospital setting should not optimal in other settings. This has a crucial impact on how models are developed for human use,” Ghassemi says. “One hospital might need the resources to coach a model, after which give you the option to show that it performs well, possibly even with specific fairness constraints. Nevertheless, our research shows that these performance guarantees don’t hold in recent settings. A model that’s well-balanced in a single site may not function effectively in a unique environment. This impacts the utility of models in practice, and it’s essential that we work to handle this issue for many who develop and deploy models.”
Ghassemi’s work is informed by her identity.
“I’m a visibly Muslim woman and a mother — each have helped to shape how I see the world, which informs my research interests,” she says. “I work on the robustness of machine learning models, and the way an absence of robustness can mix with existing biases. That interest just isn’t a coincidence.”
Regarding her thought process, Ghassemi says inspiration often strikes when she is outdoors — bike-riding in Recent Mexico as an undergraduate, rowing at Oxford, running as a PhD student at MIT, and as of late walking by the Cambridge Esplanade. She also says she has found it helpful when approaching an advanced problem to think in regards to the parts of the larger problem and check out to grasp how her assumptions about each part is likely to be incorrect.
“In my experience, probably the most limiting factor for brand spanking new solutions is what you’re thinking that ,” she says. “Sometimes it’s hard to get past your personal (partial) knowledge about something until you dig really deeply right into a model, system, etc., and realize that you simply didn’t understand a subpart accurately or fully.”
As passionate as Ghassemi is about her work, she intentionally keeps track of life’s larger picture.
“Whenever you love your research, it could be hard to stop that from becoming your identity — it’s something that I believe a variety of academics have to pay attention to,” she says. “I attempt to be certain that that I actually have interests (and knowledge) beyond my very own technical expertise.
“Among the finest ways to assist prioritize a balance is with good people. If you’ve family, friends, or colleagues who encourage you to be a full person, hold on to them!”
Having won many awards and far recognition for the work that encompasses two early passions — computer science and health — Ghassemi professes a faith in seeing life as a journey.
“There’s a quote by the Persian poet Rumi that’s translated as, ‘You might be what you’re on the lookout for,’” she says. “At every stage of your life, you’ve to reinvest find who you’re, and nudging that towards who you need to be.”
