Academic Research
I have worked on the following areas in my academic life.
Monte Carlo methods: sampling, particle filtering.
Bayesian inference: scalable inference, Bayesian computation
Applied statistics
Broadly speaking, this is related to algorithms to tackle high-dimensional likelihood-based statistical problems. This involves developing and theoretically studying novel statistical methods (Bayesian and/or frequentist) for analyzing high-dimensional and complex data. In the Bayesian context, this also involves designing new classes of posterior approximation algorithms, based on sampling and other approaches, which can produce low-error and targeted approximations rapidly even when the data are enormous. A particular theme in my research has been the study and development of scalable Monte Carlo algorithms for Bayesian inference. Accurate and fast algorithms are critical in the era of big data, where traditional Markov chain Monte Carlo (MCMC) algorithms are often too slow.
Industry work
Causal inference in practice: I am interested in causal inference and machine learning techniques for causal inference. This is of importance in industry with many application areas such as understanding the causal impact of showing advertisements on customer behaviour, the causal impact of changing customer experience on a website on customer behaviour, etc.
Machine Learning for ads modeling: I use statistics and machine learning algoeithms to work with ads data in Google.