Tamara Broderick, a once participant of the Women’s Technology Program at MIT, found her path back to the institution years later, but as a faculty member. Broderick, a tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS), works in the field of Bayesian inference, which is a statistical approach to measure the uncertainty and the robustness of data analysis techniques.
In addition to her affiliation with the EECS, Broderick is also a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society. In her lab at MIT, she and her team collaborate with experts from diverse fields to apply their research to real world problems. For example, they have worked with oceanographers to develop a machine learning model that can predict ocean currents with greater accuracy and with degenerative disease specialists to develop a tool that aids severely motor-impaired individuals to use a computer’s graphical user interface.
Broderick’s fascination with math began in her childhood, and she brought this passion into her adult life as she integrated her mathematical knowledge with physics and computer science. After graduating from Princeton University with an undergrad math degree, she moved to the UK to study at the University of Cambridge where she earned a masters in Mathematics and another in Physics.
During her time at Cambridge, Broderick took several statistics and data analysis classes and had her first experience with Bayesian data analysis. On her return to the States, she joined Professor Michael I. Jordan’s lab at the University of California’s Berkeley campus as a grad student earning a Ph.D. in Statistics. Broderick found her place in MIT because of its collaborative environment and the passionate colleagues she works with which encourages her creativity and exploration.
In one of her recent projects, she worked with an economist to study microcredit programs in impoverished areas. They used machine-learning to develop a method that determines how many data points need to be dropped to affect the ultimate conclusion of the study. Broderick finds satisfaction in exploring the balance between acceptance and elimination of data, constantly questioning if the data analysis conclusions can be generalised to new scenarios. Apart from her academic pursuits, Broderick is an outdoor enthusiast who enjoys hiking and exploring new trails with her husband.
