Expertise: econometrics, big data, machine learning, energy and environmental economics, health economics, public policy

Matthew Harding is an econometrician who develops cutting edge statistical methods for the analysis of big data to answer crucial economic questions related to individual consumption and choices in areas such as health and energy. As a data scientist, he 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 technology and automation play in inducing behavior change and helping individuals live happier and healthier lives. At the same time, his research emphasizes solutions for achieving triple-win strategies - solutions that not only benefit individual consumers, but are also profitable for firms and have a large positive impact on society at large.

He 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.

His work has been published in the Journal of Econometrics, American Economic Journal, and The Annals of Applied Probability, to name a few.

Harding received his undergraduate degree from the University College of London, his master’s from the University of Oxford, and his Ph.D. from the Massachusetts Institute of Technology. He comes to UCI following previous faculty positions at Duke University and Stanford University. He’s excited to join the UCI faculty and interact with scholars across different disciplines - engineering, statistics, economics - to study how machine learning, artificial intelligence and economics can work together to create new methods and insights for global problems.

 

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