Statistical inference is the computational process of turning data into statistics, prediction
and understanding. I work with richly structured data, such as those
extracted from texts, images and other
spatiotemporal signals. In recent years I have gravitated toward a
field in statistics known as Bayesian nonparametrics, which provides
a fertile and powerful mathematical framework for the development of
many computational and statistical modeling ideas. The spirit of
Bayesian nonparametric statistics is to enable the kind of
inferential procedures according to which both the statistical modeling
and computational complexity may adapt to increasingly large and
complex data patterns in a graceful and effective way.
In this framework, stochastic processes and random measures, along with
latent variable models such as mixture, hierarchical and graphical
models figure prominently.
My motivation for all this came originally from an interest in
machine learning, which continues to be a major source of research interest.
A primary focus in my machine learning research is to develop more effective
inference algorithms using variational, stochastic and geometric viewpoints.
Big Data Summer Institute
at the University of Michigan. Exciting opportunity
for computer science, mathematics and statistics undergraduates
looking to find meaning in very large scale data.