I am a Senior Lecturer in the Statistics Department. My broad research interests lie in the theory and application of computational statistics, machine learning and data science methods.  One of my overarching goals is to develop stronger connections between the mathematical & statistical foundations of these methods and their applications.  I am motivated by 1) how applications can inspire new theory and 2) how theory be developed in a more practically relevant way.              

My research has primarily focused on 1) Monte Carlo methods for sampling and sequential Bayesian inference in continuous and discrete time; 2) stochastic analysis of interacting particle methods in nonlinear filtering and gradient flow based methods 3) methods for quantifying model uncertainty from data and 4) real-time estimation of non-stationary model parameters.  I am particularly motivated by applications in the environmental and biomedical sciences. 

For more information see my personal webpage.