Jonathan Niles-Weed
Deputy Director & Associate Professor of Mathematics and Data Science
Bio: Jonathan Niles-Weed is an Associate Professor of Mathematics and Data Science at New York University. He studies mathematical statistics, the mathematics of data science, and applications of optimal transport in statistics, probability, and machine learning. Prior to joining NYU, he earned his PhD from the Massachusetts Institute of Technology and spent a year as a Postdoctoral Member at the Institute for Advanced Study.
Research Areas:
- High-dimensional statistics
- Optimal transport
- Information theory
Awards:
- Google Research Collabs; Optimal Transport and Machine Learning Workshop, Best Paper Award for Entropic estimation of optimal transport maps, 2020 (with CDS PhD student Aram-Alexandre Pooladian) (2021)
- Institute of Mathematical Statistics Tweedie New Researcher Award, Alfred P. Sloan Research Fellowship (2022)