Matthew is an Econometrician and Data Scientist who develops techniques at the intersection of machine learning and econometrics to answer Big Data questions related to individual consumption and investment decisions in areas such as health, energy, and consumer finance. He often focuses on the analysis of "Deep Data", large and information-rich data sets derived from many seemingly unrelated sources but linked across individuals to provide novel behavioral insights. He is particularly interested in the role of technology and automation to induce behavior change and help individuals live happier and more sustainable lives. At the same time his research emphasizes solutions for achieving triple-win strategies. These are solutions that not only benefit individual consumers, but are profitable for firms, and have a large positive impact on society at large.

As an Econometrician he is currently exploring the potential of machine learning methods in Economics. He is interested in the estimation of high-dimensional models and the use of deep learning methods to produce interpretable economic insights. He also designs and evaluates large scale field experiments in collaboration with industry leaders to measure the individual and social consequences of individual choices and the extent to which Big Data can be used to improve choices and lead to more accurate and targeted programs and products. His research relies on terabyte sized data sets of individual choices and consumption profiles, to build a comprehensive framework for understanding economic behavior and develop new strategies for achieving triple-win solutions.