Learning a subject well means moving beyond the recitation of facts to a deeper knowledge that can be applied to new problems. Designing computers that can transcend rote calculations to more nuanced understanding has challenged scientists for years.
Only in the past decade have researchers' flexible, evolving algorithms—known as machine learning—matured from theory to everyday practice, underlying search and language-translation websites and the automated trading strategies used by Wall Street firms.
These applications only hint at machine learning's potential to affect daily life, according to John Lafferty, the Louis Block Professor in Statistics and Computer Science. With his two appointments, Lafferty bridges these disciplines to develop theories and methods that expand the horizon of machine learning to make predictions and extract meaning from data.
"Computer science is becoming more focused on data rather than computation, and modern statistics requires more computational sophistication to work with large data sets," Lafferty says. "Machine learning draws on and pushes forward both of these disciplines."
Lafferty's work focuses on the theories and algorithms that power machine learning. The goal is to develop computer programs that, with little or no human input, can extract knowledge from large amounts of numbers, text, audio or video and make predictions and decisions about events that haven't been coded in its instructions.
"The classical areas of applied mathematics, including partial differential equations, developed from the study of physical processes such as fluid flow," Lafferty says. "What we're seeing now is that entirely new directions in applied mathematics are opening up from the study of modern large data sets."