Ongoing Research Themes
High-Dimensional Statistical Learning
High-dimensional data, in which the number of features exceeds the number of observations, results in both theoretical and methodological challenges. We develop approaches to overcome these challenges by exploiting structure in the data or in the underlying model. We are particularly interested in unsupervised learning, with a focus on graphical modeling.
Statistical Models for Neural Activity
Recent technological advances have made it possible to simultaneously record from huge numbers of neurons. This leads to a number of questions:
what is the functional connectivity among a population of neurons?
can we identify functional sub-populations of neurons?
how can we model a neuron’s activity as a function of covariates?
Changepoint Detection and Inference
In many settings, it is natural to wonder whether the underlying data-generating model changes at distinct points in time. We are developing fast algorithms to detect such changepoints, as well as techniques to quantify the uncertainty associated with these estimated changepoints.
It is becoming increasingly common for researchers to collect multiple data views — that is, sets of features — on a single set of observations. For instance, researchers might collect clinical as well as gene expression measurements for a single set of patients. We are developing approaches to exploit the availability of multiple data views in order to answer questions that could not be answered if each data view were collected on a separate set of observations.
Our work is motivated by diverse applications both in and out of the biomedical sciences. It has recently been inspired by collaborations with researchers in genomics, neuroscience, microbial ecology, and pathology. We are always open to new and interesting collaborations!