I am an Associate Research Scientist in the Applied Physics and Mathematics department at Columbia University and affiliated with its Data Science Institute. I have specialized in statistical machine learning, computational Bayesian statistics, approximate inference methods, and sequential decision processes.
My research focuses not only on the development of Bayesian probabilistic models and algorithms, but also on their application to a wide range of disciplines. I am currently working on descriptive, predictive, and prescriptive modeling with applications in science, engineering and healthcare.
I was a data-science postdoc at Columbia University from 2016 to 2018 working with Prof. Chris Wiggins and Prof. Noémie Elhadad on statistical machine learning for healthcare data, in the context of electronic health records and self-tracked data.
I completed my Ph.D. in Electrical Engineering at Stony Brook University in 2016 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).