Cynthia Rudin

Nobel Conference 57

Cynthia Rudin

Imagine a list of 8 billion numbers, one piece of data for each person on planet Earth. How would you go about calculating something like the average of those 8 billion pieces of data? There’s no envelope big enough or pencil sharp enough to do it by hand, but such a calculation is straightforward with the help of a simple computer program. Now, however, imagine trying to find complex, unknown, or even unexpected patterns in that list of 8 billion numbers; the task has just gotten exponentially harder. For that, we need machine learning, the form of artificial intelligence in which computer programs automatically adjust their own programming to handle these more difficult tasks. Cynthia Rudin brings to Nobel Conference 2021 the ability to explain the mechanisms of machine learning and to show the utility of its application to a variety of areas. 

At its core, machine learning uses new data along with previous experience to automatically change the behavior of computer programs, without human interaction. Many machine learning programs do this in a way that is nearly (or entirely) impossible for a human to understand, due to their self-changing nature and high complexity; they’re called “black box models” in the field. Cynthia Rudin studies and advocates for the use of interpretable models--machine learning programs that humans can understand, but that can be as efficacious as their black box counterparts. Among the collaborative projects on which she works, she led the first major effort to maintain a power distribution network with machine learning (in New York City), and she developed algorithms for crime series detection, which allow police detectives to find patterns of housebreaks.

Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke. She is also the principle investigator of the Prediction Analysis Lab, the focus of which is interpretable machine learning. Rudin is a fellow of both the American Statistical Association and the Institute of Mathematical Statistics. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, She holds a PhD in applied and computational mathematics.