Theoretical and algorithmic aspects of high-dimensional statistics, information theory, and optimization

Yihong Wu is broadly interested in the theoretical and algorithmic aspects of high-dimensional statistics, information theory, and optimization, with application to machine learning and data science.

Methods

deep learning ;  dimensionality reduction ;  graph theory ;  network analysis ;  neural networks ;  optimization ;  statistical methods ;  language models  

Topics

algorithms ;  artificial intelligence ;  machine learning ;  network analysis  

Biography

Wu is the James A. Attwood Professor and Department Chair of Statistics and Data Science at Yale University. He received his BE degree from Tsinghua University in 2006 and his PhD degree from Princeton University in 2011. He was a postdoctoral fellow in the Statistics Department at The Wharton School, University of Pennsylvania, from 2011 to 2012, and an assistant professor at the University of Illinois at Urbana-Champaign from 2013 to 2016. He was elected an IMS fellow in 2023 and was a recipient of the NSF CAREER award in 2017 and the Sloan Research Fellowship in Mathematics in 2018.