معرفی
Jonathan Niles-Weed is an Associate Professor of Mathematics and Data Science at New York University, affiliated with the Courant Institute of Mathematical Sciences and the Center for Data Science. He is a core member of the Math and Data and STAT groups, focusing on mathematical statistics, data science, and optimal transport applications.
- PhD in Mathematics and Statistics from Massachusetts Institute of Technology
Research Interests: His work centers on statistical and computational challenges in high-dimensional data, optimal transport theory, and information theory. He has pioneered the development of a statistical theory for optimal transport in machine learning and probability.
Scientific Awards:
- Best Paper Award (2020) at the Optimal Transport and Machine Learning Workshop
- Institute of Mathematical Statistics Tweedie New Researcher Award (2021)
- Alfred P. Sloan Research Fellowship (2022)
- NSF CAREER Award
- SIAM Activity Group on Data Science Early Career Prize (2024)
Advising and Grants: He supervises PhD students, including Aram-Alexandre Pooladian. His research is supported by the National Science Foundation, Google Research, and the Alfred P. Sloan Foundation.
