About

Matthew Dixon is an Assistant Professor of Applied Mathematics at the Illinois Institute of Technology and an Affiliate at Stuart Business School. With over 17 years of relevant experience, he specializes in teaching graduate courses in the Masters in Applied Math, Masters in Mathematical Finance, and Masters in Analytics program. His courses include computational finance, computational math, Bayesian econometrics, and machine learning. Matthew is also an active researcher at the interface of machine learning, statistics, and applied math in financial applications. His research is funded by Intel Corp and focuses on developing innovative solutions for financial institutions using cutting-edge technologies. Prior to his current position, Matthew worked as a Principal Consultant at Quiota LLC, where he provided Quant R&D consulting services to asset management firms, prop trading groups, and fintech companies in the US. His projects ranged from designing and building multi-asset risk management libraries in C# to statistical modeling and machine learning for asset management and optimal execution in Python/R. Matthew also served as an Assistant Professor of Analytics & Finance at the University of San Francisco, where he taught graduate-level courses in the MS in Analytics (MSAN) and MS in Financial Analysis (MSFA) programs. He also served as Director of the Practicums in the MSAN program, an industry project course partnering with around 15 Bay Area companies to apply statistical and machine learning techniques to big data for various purposes, including fraud detection, traffic prediction, and power outage prevention. Matthew holds an MSc in Parallel and Scientific Computation from the University of Reading, where he specialized in developing parallel algorithms for scientific computing. He is proficient in various technologies, including ML, research scientist, and big data.