My research

My current research develops general principled frameworks for robust statistical inference and exploratory data analysis that have applications in a variety of fields like genomics, neuroscience, and social science. I am developing theoretically motivated Bayesian and frequentist frameworks to robustly fit likelihood-based models that may be misspecified. This methodology is relevant in the current age where we tend to have big data that is potentially biased or corrupted (e.g. arising in surveys, health records, etc.). I have also developed new exploratory data analysis methods, e.g. those based on novel Bayesian density-based clustering and iterative-testing to find combinatorial structures (e.g. networks) for integration of multi-view data. Given my strong background in stochastic processes and large deviation theory, in the future I aim to develop methods for analysis of time series data and for data assimilation problems that arise in climate modeling.