Professor Amir Ali Ahmadi has received the SEAS Innovation Award for his proposal titled “Nonconvex Polynomial Optimization for Machine Learning in Finance”. This grant is funded by the generous donations of Princeton alumni, parents and other donors.
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).
Princeton students currently enrolled in a Ph.D or Master’s program are eligible for this certificate. For more information or to enroll, please visit our Graduate Certificate Program page.
Tools like social media, cell phones, and other digital marvels have changed how we live, but they have also enabled scientists to collect and process data on human behavior on a huge scale. In Bit by Bit (Princeton University Press), one of the top computational social scientists, Matthew Salganik, provides a blueprint for how to use...