Machine Learning

Understanding, managing and using data is increasingly important in nearly every industry, government sector, and academic domain. Data science — including big data, data analytics, machine learning and artificial intelligence — is an interdisciplinary, collaborative research domain.

Our group focuses on the development of methods that turn data into knowledge. Framing questions statistically allows us to leverage data resources to extract knowledge and obtain better answers. The central dogma of statistical inference, that there is a component of randomness in data, enables us to formulate questions in terms of underlying processes and to quantify uncertainty in the answers. It also allows us to establish methods for prediction and estimation and to quantify the degree of certainty, all using algorithms that exhibit predictable and reproducible behavior.

Faculty

Eric Chi
Jacqueline Hughes-Oliver
Jessie Jeng
Eric Laber
Wenbin Lu
Brian Reich
Rui Song
Alyson Wilson
Yihui Zhou