Associate Professor of Econometrics and Statistics
- Statistical machine learning- Probabilistic graphical models - Dynamic networks estimation - High-dimensional estimation and inference - Stochastic optimization with constraints - Distributed optimization and federated learning My research interests revolve around i) developing statistical and machine learning models that facilitate scientific discovery from high-dimensional noisy data, as well as ii) efficient optimization procedures for fitting these models to large amounts of data. I am interested in developing computationally efficient and theoretically sound techniques that lead to machine learning systems that do not just predict, but also uncover insightful patterns and explain underlying data generating mechanisms. Towards that end, my group frequently works within the framework of probabilistic graphical models, focusing on dynamic network modeling, quantification of uncertainty in high-dimensional settings, differential network analysis, and networks for event data. My recent efforts on efficient and scalable optimization methods for statistical machine learning have focused on the development of stochastic algorithms for nonlinear problems with constraints and distributed optimization methods that are relevant for federated learning.