We are a research group at New York University, wishing to understand the foundations of generalization, learning and decision making towards developing intelligent systems. We often (1) take a probabilistic approach; (2) care about automatically discovering scientifically interpretable structure in data; and (3) are excited about connections with physics, numerical methods and scientific computing. We are particularly active in building methods for probabilistic deep learning, scalable Gaussian processes, kernel learning, and training of deep neural networks. We believe in open and reproducible research. If you'd like to try out these methods check out our code page.
Andrew Gordon Wilson
Currently considering applications
Ke Alexander Wang
Ben Athiwaratkun (PhD; now at Amazon AI Research)
Patrick Nicholson (Masters)
Michael Luo (Masters)