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The Center for Statistics and Machine Learning is a focal point for education and research in data science at Princeton University. By its nature, CSML is an interdisciplinary enterprise. The center’s mission is to foster and support:

  • a community of scholars addressing the manifold challenges of modern data-driven exploratory research
  • the development of innovative methodologies for extracting information from data
  • the education of students in the foundations of modern data science

The center supports and collaborates on research and teaching that combines insights from computation, machine learning, and statistics with specific application domains. To encourage a flow of ideas, CSML welcomes connections with faculty, departments, centers and institutes across the Princeton campus.  In addition to exploring novel applications, the center supports innovations in the theoretic foundations of data science, including advanced algorithms for big-data problems, machine learning, optimization, and statistics.

Established in July 2014, the Center for Statistics and Machine Learning is part of a rich and influential history in data science at Princeton University. Individuals such as Samuel Wilks, John Tukey, William Feller, Alonzo Church, Alan Turing, and John Von Neumann played key roles in pioneering the use of statistics, probabilistic models, and computers to solve real world problems. The Cooley–Tukey FFT algorithm (1965), and the initiation of the ImageNet database (2009) are two prominent examples of Princeton’s prior contributions to data science.

The center is housed at 26 Prospect Avenue (Bendheim Center for Finance Building, and formerly Dial Lodge).


As an increasing number of research groups across the disciplines find themselves relying on data-driven predictive insights only possible through machine learning applications, a nagging problem has arisen.

Picture yourself trying to build a machine to detect email spam. You might start with simple rules that identify key words such as “drugs,” for instance. Of course, some legitimate emails could contain those key words, so you add rules to take into account these cases. In an adaptive world, advertisers may quickly learn the rules of your system...

 

 

Stress can run high for students during final exams, so we put our heads together to plan an event where our students could relax and recharge.

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