His work spans a wide range of topics in computational science and engineering, with a particular focus on the analysis and design of complex physical and biological systems using machine learning, stochastic modeling, computational mechanics, and high-performance computing. Prior to Penn, he spent two years as a post-doctoral researcher at MIT developing machine learning algorithms that synergistically combine heterogeneous data with prior knowledge emanating from first physical principles towards establishing a new paradigm in predictive modeling and decision making under uncertainty.