Tamara Broderick’s career at MIT started when she was still in high school. In 2002, she participated in the inaugural class of MIT’s Women's Technology Program (WTP) — a new experiment at the time that brought high school girls to campus for a four-week program designed to spark a passion for engineering and computer science. She arrived on campus that summer from Cleveland, Ohio, thrilled at the prospect of an immersion in science and math. “WTP was an amazing opportunity to learn the basics of computer science, electrical engineering, and applied math with like-minded peers,” she says. “I felt I was suddenly in a place where I really belonged.”
Now the ITT Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science, Broderick recalls “open-ended projects that pushed frontiers for me,” such as biopsying a grape suspended in Jell-O with a homemade Lego-based probe. There was extracurricular fun as well with a “group of people who shared the same nerdy sensibility.” She saw the Boston Pops, ate her first dim sum, and drew the digits of pi on a beach. But most of all Broderick credits WTP with demonstrating the potential of computer science and mathematics as fields of study. “My experience at WTP was formative,” she says. “It had a broad impact on what I wanted to do, in my decision to pursue math and computer science more generally.”
Broderick set out to learn more about physics, math, and computer science, earning a BA in mathematics from Princeton University in 2007. She went on to earn a master of advanced study in mathematics in 2008 and an MPhil in physics in 2009 at Cambridge University. While on a Marshall Scholarship in the UK, Broderick discovered Bayesian statistics. “A lot of my interests coalesced,” she says. She then came back to the U.S. in 2009, to the University of California at Berkeley, to begin a master’s in computer science (which she completed in 2013) and a PhD in statistics, which she earned in 2014.
Her doctoral work in machine learning involved developing algorithms that can speed computational analysis of large, streaming data sets, enabling computers to discover more and more hidden and meaningful structure in data as more data is obtained — without the need for prior human annotation.
Though Broderick describes her work as primarily theoretical, it is already lending itself to useful and surprising applications. One of her algorithms has been deployed to analyze tumor heterogeneity. Different types of cancer may appear in a single tumor, and identifying the disparate yet connected types of diseased tissue may speed a more targeted treatment approach.
This algorithm and the theory behind it was the work Broderick presented at MIT in 2013 during another recruitment program devoted to increasing gender diversity, Rising Stars. Initially developed in the Department of Aeronautics and Astronautics, the program showcases the research of female PhDs and postdoctoral candidates from around the U.S. Rising Stars gave Broderick a second taste of the dynamic academic environment that had made such an impression on her a decade earlier. “There are so many amazing people at MIT at the top of their game,” she says. “And it’s a place that helps you start exchanging ideas, and shows you that research isn’t a thing you do alone, but with a lot of other people.”
Broderick’s focus on machine learning and statistics, on display at this workshop, opened the door to her current position. On top of assignments that include a new graduate course in theoretical statistics, and work for MIT’s new Institute for Data, Systems, and Society, Broderick has an additional item on her agenda: “I’m giving a talk at WTP this summer,” she says, “introducing students to the kind of research I do.”
