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Princeton University established the Center for Statistics and Machine Learning (CSML) in July 2014 to serve as the primary organization on campus for education and research activities in statistics, machine learning, and the data sciences.
CSML will be an interdisciplinary group with research focused around methodological challenges at the intersection of these fields. CSML will also be deeply connected to real-world application areas, such as in astrophysics, economics, finance, genomics, neuroscience, political science, public policy, and sociology.
Princeton University has a rich and influential history in statistics and machine learning, with individuals such as Samuel Wilks, John Tukey, William Feller, Alonzo Church, Alan Turing, and John Von Neumann having played key roles in pioneering the use of statistics, probabilistic models, and computers to solve real world problems. Based on a foundation formed by Samuel Wilks, the Department of Statistics existed at Princeton University during 1965-1985, with John Tukey being its inaugural chair. Princeton has educated many highly influential leaders in statistics.
CSML is excited to be reviving an official statistics organization at Princeton and to be doing so in conjunction with machine learning. We will be taking a forward-looking approach to these fields and the many exciting activities involving modern data.
CSML will offer a Ph.D. degree and a graduate certificate program in statistics and machine learning in the future. There are currently several Ph.D. programs at Princeton University where it is possible to involve statistics and machine learning in one's Ph.D. education and dissertation. These include computer science, economics, electrical engineering, operations research and financial engineering, applied and computational mathematics, and quantitative and computational biology.