Matthew Dixon

  • Assistant Professor of Applied Mathematics
  • Affiliate Assistant Professor, Stuart School of Business

Matthew Dixon is a British applied mathematician working in the area of algorithmic finance. His research focuses on applying concepts in computational and applied mathematics to financial modeling, especially in the area of algorithmic trading and derivatives. Matthew's research is currently funded by Intel Corporation and he develops codes for high performance architectures. His work in deep learning with Diego Klabjan (NWU) has brought wide recognition and he is a frequently invited speaker at quant and fintech events around the world in addition to be referenced as a computational finance expert in multiple reputed media outlets including the Financial Times and Bloomberg Markets.


Ph.D. Imperial College, London, Applied Mathematics, Mathematics Department
M.Sc. University of Reading, Parallel and Scientific Computation (with distinction)
M.Eng. Imperial College, London, Civil and Environmental Engineering

Research Interests

Fast and scalable computation for financial modeling
Predictive analysis
High-volume data analysis
Financial econometrics; risk management
Machine learning
Quantitative analytics
Algorithmic trading
Monte-Carlo simulations


Faculty Innovation Award, Fall 2018


  • M. Dixon and S. Crepey, Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations, to appear in the Journal of Computational Finance, 2019. 
  • I. Halperin and M.F. Dixon, "Quantum Equilibrium-Disequilibrium”: Asset Price Dynamics, Symmetry Breaking and Defaults as Dissipative Instantons, to appear in Physica A: Statistical Mechanics, 2019. 
  • C. Akcora, M.F. Dixon, Y. Gel and M. Kantarcioglu, Blockchain Analytics for Intraday Financial Risk Modeling, Digital Finance, Aug 2019. 
  • C. Akcora, M.F. Dixon, Y. Gel and M. Kantarcioglu, Blockchain Data Analytics, to appear, IEEE Intelligent Informatics Bulletin, 2019. 
  • C. Akcora, M.F. Dixon, Y. Gel, and M. Kantarcioglu. Bitcoin Risk Modeling With Blockchain Graphs. To appear in Economic Letters, 2018. 
  • M.F. Dixon, N. Polson, and V. Sokolov. Deep Learning for Spatial-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading. To appear in Applied Stochastic Models in Business and Industry, 2018.
  • M.F. Dixon. A High Frequency Trade Execution Model for Supervised Learning. High Frequency, 1(1), pp. 32-52, 2018.
  • M.F. Dixon. Sequence Classification of the Limit Order Book using Recurrent Neural Networks. J. Computational Science 24, pp. 277-286, 2017. 
  • M.F. Dixon, D. Klabjan, and J. H. Bang. Classification-based Financial Markets Prediction using Deep Neural Networks. Algorithmic Finance 6(3-4), pp. 66-99, 2017. 
  • M.F. Dixon, J. Chong and K. Keutzer. Accelerating Value-at-Risk Estimation on Highly Parallel Architectures. Concurrency Computat.: Pract. Exper 24(8), Wiley, pp. 895-907, 2012.


Intel funded research in computational finance


Master of Mathematical Finance


Computational finance, statistical machine learning, scientific computing, fintech