معرفی
Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation.
His research spans theoretical and applied statistics, with core interests in:
- Foundational inference: Nonparametric methods, asymptotic theory, causal frameworks
- Modern applications: Machine learning, high-dimensional statistics, astrostatistics
- Interdisciplinary domains: Bioinformatics, genomics, physical sciences via the STAMPS group
His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees.
Award highlights include:
- COPSS Presidents' Award (1999) - Top honor for statisticians under 40
- CRM-SSC Prize (2002) - Landmark contributions to statistics
- Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS
He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.
