If you are not redirected automatically, follow this link to example. Debdeep Pati : Introduction

I am an Assistant Professor of Statistics at Florida State University. I obtained a PhD in Statistics at Duke University on May, 2012 under the supervision of Prof. David Dunson. Here is the link to my dissertation. Prior to this, I obtained a Bachelors degree in 2006 and a Masters degree in 2008 from Indian Statistical Institute, Kolkata.

My research interests center around both theoretical and methodological aspects of Bayesian statistics. I am particularly focused on nonparametric Bayesian foundational theory and methodology in a broad range of areas including density estimation, high-dimensional density regression and variable selection, shape reconstruction, imaging and hierarchical modeling of shapes.

I enjoy developing methodology that has an immediate motivation and impact to a particular application area, while being broadly applicable and leading to foundational theoretical questions. In the nonparametric Bayes paradigm this often involves developing new classes of flexible prior distributions for densities, conditional densities or functions. It is fascinating to explore the structure of the spaces on which the priors are supported while studying how the posterior concentrates as increasing amounts of data are collected. Studying these spaces becomes more challenging outside of unconstrained Euclidean spaces, such as in studying closed surfaces and other shapes, and when the dimension explodes. Much of my ongoing work is based on theoretical investigation of the posterior distribution in Bayesian high-dimensional models, as seemingly innocuous prior choices can lead to misleading inferences in high-dimensional settings.

I enjoy modeling of distributions of objects contained within pixelated images, with applications ranging from tumor tracking for targeted radiation therapy, to classifying cells in the brain, to new landmark free techniques for 3d animation and so on. I also take interest in model based clustering techniques, spatial statistics, particularly in point pattern data modeling and also datasets subject to preferential sampling.

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