I am an Associate Research Scientist in the Applied Math department at Columbia University and the Data Science Institute. I am currently working on descriptive, predictive, and prescriptive modeling for electronic health records, in collaboration with Prof. Chris Wiggins and Prof. Noémie Elhadad
I have specialized in statistical data processing, Bayesian Theory, approximate (Monte Carlo and Variational) inference methods, and reinforcement learning (e.g. the multi-armed bandit). My research focuses not only on the development of Bayesian probabilistic models, but also on their application to a wide range of disciplines.
I completed my Ph.D. in Electrical Engineering at Stony Brook University under the supervision of Prof. Petar M. Djurić. My dissertation is entitled "Sequential Monte Carlo methods for inference and prediction of time-series". My research attracted interest in econometrics (for prediction in stochastic volatility models) and biomedicine (for the study of fetal heart-rate signals).