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Matthew Dixon Biography

Matthew Dixon

Matthew Dixon
Assistant Professor of Finance

Education: 

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

Biography: 

Matthew Dixon is an Assistant Professor of Finance. His research focuses on the application of advanced computational techniques such as machine learning and high performance computing to compute and data intensive financial applications, especially in the areas of algorithmic trading, derivative modeling and risk analysis. Matthew's research is currently funded by Intel Corporation and he has been referenced as a computational finance expert in multiple reputed media outlets and trade shows including the Financial Times and the Chicago Trade Show.

He has contributed to the R package repository and published around twenty peer-reviewed technical articles. Matthew's teaching style is orientated towards ensuring that his students reach their full potential in the job market. He has taught financial econometrics, derivatives, machine learning and text mining at the University of San Francisco and held visiting appointments in CS/Math at Stanford University and UC Davis.

Matthew is the co-founder of multiple financial technology start-ups including the Thalesians, a
financial educational event management and consulting company which is a member of
Level39, Europe's biggest financial technology Incubator. Prior to joining academia, he has held
industry appointments as a quant at banks such as Lehman Brothers, the Bank for International
Settlements and Barclays Capital. He chairs the workshop on computational finance at the
annual SuperComputing conference and serves on the program committee of HPC and on the
editorial board of the Journal of Financial Innovation. Matthew holds a MEng in Civil
Engineering from Imperial College London, a MSc in Parallel and Scientific Computation (with
distinction) from the University of Reading, and a PhD in Applied Math from Imperial College
London. He became a chartered financial risk manager in 2014.

Research Interests: 

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