Broadly speaking, my research lies at the intersection of Statistics, Applied Probability and Machine Learning, with a particular focus on Bayesian computation. More specifically I have been working on:
Computational Statistics and Machine Learning: Monte Carlo methods, Markov Chain Monte Carlo algorithms, variational inferences, computational complexity of Bayesian methods;
Bayesian modeling: Bayesian Nonparametrics, Hierarchical models, Random Partition Models;
Probability Theory: Stochastic processes, Markov chains, point processes and random measures;
Applied Statistics: clustering, record linkage, spatial statistics, variable selection.
