About

About CSML

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.  

We are currently in the process of planning CSML, so please check back often for updates.


Tuesday, Sep 2, 2014
The 2014 Guy Medal in Silver was awarded to Jianqing Fan by the Royal Statistical Society for (1) his seminal papers Sure independence screening for ultra-high dimensional feature space and Large covariance estimation by thresholding principal orthogonal complements (read to
Tuesday, Jul 22, 2014
Princeton University has established the Center for Statistics and Machine Learning. John Storey, a professor of molecular biology and the Lewis-Sigler Institute for Integrative Genomics, has been named the center's director.

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