Tamara Broderick first set foot on MIT’s campus when she was a high school student, participating in the inaugural Women’s Technology Program. This monthlong summer academic experience provides young women with a hands-on introduction to engineering and computer science. The question of the probability of her returning to MIT years later as a faculty member could potentially be answered quantitatively using Bayesian inference, a statistical approach to probability that aims to quantify uncertainty by continuously updating assumptions as new data are obtained. In her lab at MIT, Broderick, now a newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS), utilizes Bayesian inference to quantify uncertainty and measure the robustness of data analysis techniques. She is also a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.
Broderick’s focus is on helping individuals understand the limitations of the statistical tools available to them and sometimes collaborating to create better tools for specific situations. For example, her group collaborated with oceanographers to develop a machine-learning model for more accurate predictions about ocean currents. In another project, she worked with degenerative disease specialists on a tool to help severely motor-impaired individuals utilize a computer’s graphical user interface by manipulating a single switch. Collaboration is a common theme in her work, as she believes that working across disciplines allows for constant learning and application of machine learning in various fields.
Broderick’s passion for math started at a young age in Cleveland, Ohio. She was enrolled in the Center for Talented Youth program, where she took summer classes on subjects like astronomy, number theory, and computer science. At Princeton, she struggled to choose between math, physics, and computer science, ultimately pursuing an undergraduate math degree while taking physics and computer science courses. After studying in the UK and earning a PhD in statistics at UC Berkeley, she joined MIT as a faculty member attracted by the collaborative environment and the passion of her colleagues.
Broderick’s research focuses on applying Bayesian data analysis to solve real-world problems that matter to people. One of her recent projects involves collaborating with an economist to study the impact of microcredit programs on impoverished areas. She and her team developed a method using machine learning to determine the fragility of study results and how removing data points can affect conclusions. This work aims to provide researchers with tools to assess the reliability and generalizability of their study results.
Outside the lab, Broderick enjoys hiking with her husband and collecting patches earned by exploring all the trails in a park or trail system. This hobby combines her love for the outdoors with an adventurous spirit for discovering new areas. Just as in her research, curiosity, open-mindedness, and a passion for problem-solving drive her exploration both in and out of the lab.
