Matthew Dixon

Matthew Dixon is a highly skilled researcher and innovator who specializes in mathematical algorithms for prediction and outlier detection, with a focus on risk. He is a published author of a "Machine Learning in Finance" textbook and several journal papers on algorithms and models for machine learning, blockchain-based technologies in fintech. His research has been featured in publications such as the Financial Times, Bloomberg Markets, and Barron's Advisor. Dixon has also received various awards for his research, including the Risk Magazine's Buy-side Quant of the Year (2022) and the College of Computing Dean's Excellence in Research Award (Junior level) in 2021.

Dixon holds a PhD in Applied Mathematics from Imperial College London, where he also completed his MEng in Civil Engineering. He also holds an MSc in Parallel and Scientific Computing from The University of Reading. He has a wealth of experience in academia and finance, having served as an Assistant Professor of Applied Math at Illinois Institute of Technology and UC Davis and an Assistant Professor of Analytics & Finance at the University of San Francisco. He has also worked in various roles for companies like Silver Lake Kraftwerk, Barclays Capital, and Lehman Brothers.

In addition to his work, Dixon is an editorial associate for the World Scientific Annual Review of Fintech and the AIMS Journal of Dynamics & Games. He is a Chartered Financial Risk Manager (FRM) and a member of the CFA NY Quant Investing Committee. Dixon is also a Google Summer of Code Mentor for the R Statistical Computing Project (2017) and the former Chair of the IEEE/ACM Workshop on High-Performance Computational Finance (2010-2015). He is currently a technical co-founder in a stealth mode startup company.