The center will anchor the teaching of and research in statistics and machine learning on campus, Storey said, offering an undergraduate certificate as well as graduate training in the field.
Statistics and machine learning center around developing and understanding data analysis tools. Even though the disciplines have somewhat different histories, they share so much in common today that they are difficult to distinguish, Storey said.
"Given the growing importance and prominence of machine learning and statistics in both industry and academia, it is crucial for Princeton undergraduates to have access to a first-rate education in these areas," Storey said. "The interest from students here has gone up tremendously. They're living in this data-rich world."
The idea to establish a center at Princeton emerged from a campus gathering in 2011 when faculty and others met to discuss their common interest in statistics and machine learning.
"It's kind of a beautiful thing how organically it happened," Storey said. The faculty and other researchers realized how much work they were doing, how much they had in common and the increasing prominence of the field.
Storey said data-driven scientific discovery is a significant component in a wide area of study from politics and economics to neuroscience and his own field of genomics, where "we're sequencing everything we can get our hands on, generating massive amounts of data."
Like many cross-disciplinary programs at Princeton, the Center for Statistics and Machine Learning will involve faculty from various departments: Storey, Imai and others including Jianqing Fan, the Frederick L. Moore, Class of 1918, Professor in Finance and chair of the Department of Operations Research and Financial Engineering.
"The breadth and depth of the statistics and machine learning faculty at Princeton are striking," Storey said. "With this intellectual leadership in place, Princeton holds the seed needed to grow into one of the most influential research institutions in this field."