AI-Powered Economics for Real-World Change

Matthew Harding is Professor of Economics and Statistics at the University of California, Irvine. Trained at MIT, Oxford, and UCL, his work spans econometrics, machine learning, and applied microeconomics, with contributions to panel data methods, quantile regression, and Big Data methods. He has published widely in leading journals across econometrics, energy, and health economics, and co-authored the textbook Modern Business Analytics. Harding has also held appointments at Stanford and Duke, where he helped establish data analytics labs and interdisciplinary research centers, and has advised doctoral students who have gone on to leading roles in academia, government, and industry.
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His recent work integrates econometric methods with machine learning architectures to tackle problems in energy economics, policy design, and digital markets. From modeling household responses to dynamic electricity pricing to developing novel methods for choice models, his research demonstrates how statistical rigor, and computational innovation can be combined to address pressing challenges. Looking forward, Harding is developing a research agenda at the intersection of AI and energy economics, focusing on how the accelerating demand for computation and large-scale models will reshape energy markets, grid reliability, and the economics of power systems. His goal is to build econometric and AI tools that not only advance methodology but also provide actionable insights into the energy requirements of next-generation artificial intelligence.