Machine Learning and Computational Statistics

In recent years machine learning has emerged as an indispensable tool for computational science. It is also an active and growing area of study throughout the Flatiron Institute. Researchers at Flatiron are especially interested in the core areas of deep learning, probabilistic modeling, optimization, learning theory and high dimensional data analysis. They are also applying machine learning to problems in cosmological modeling, quantum many-body systems, computational neuroscience and bioinformatics. Below is a list of researchers who work in these areas; prospective visitors should feel free to contact them for more information.

Directed by Lawrence Saul (Center for Computational Mathematics) and an advisory committee consisting of Bob Carpenter (Center for Computational Mathematics), Mitya Chklovskii (Center for Computational Neuroscience), Antoine George (Center for Computational Quantum Physics), David Hogg (Center for Computational Astronomy), Olga Troyanskaya (Center for Computational Biology) and Jutta Rogal (Initiative for Computational Catalysis), the program is designed to spur further collaboration between researchers at the Flatiron Institute and in academia applying machine learning to problems of scientific interest. The program will provide support to current faculty members who wish to spend sabbaticals or leaves of absence at Flatiron and to junior researchers who are able to defer their start date at a tenure-track position in academia in order to spend a research year at the Flatiron Institute.

The program will support up to four visiting scholars at a time, with visits up to one year in length. Junior researchers will be hired as associate research scientists with one-year appointments; senior researchers will typically have a visiting appointment compatible with their university’s sabbatical leave policies. Visiting scholars will be offered subsidized housing, salary and benefits as appropriate, travel and other research-related expenses.