Associate Professor of Statistical Science
Research in high-dimensional inference, nonparametric methods, Bayesian modeling, and biostatistics. Tackling statistical and computational challenges in analyzing big data. A recent focus of my research is on using multi-scale techniques to construct flexible probability models that can be applied to massive data sets. Traditional nonparametric approaches, while enjoying many established theoretical properties, are often computationally intractable for big data. Multi-scale inference provides a general framework for tackling the computational bottleneck, while preserving the theoretical guarantees enjoyed by classical methods.
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CAREER: Advances in multi-scale Bayesian inference and learning on massive data awarded by National Science Foundation (Principal Investigator). 2018 to 2023
Effects of Aging and the Urinary Microbiome on Recurrent Urinary Tract Infections awarded by National Institutes of Health (Co Investigator). 2018 to 2021
Bioinformatics and Computational Biology Training Program awarded by National Institutes of Health (Mentor). 2005 to 2021
Graphical multi-resolution scanning for cross-sample variation awarded by National Science Foundation (Principal Investigator). 2016 to 2019
Bayesian recursive partitioning and inference on the structure of high-dimensional distributions awarded by National Science Foundation (Principal Investigator). 2013 to 2016