+44 1865 270503
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Justin is an Associate Professor of Mathematics at the University of Oxford and Director of the Oxford Masters program in Mathematical & Computational Finance. Justin's research lies at the intersection of applied mathematics, machine learning, and high-performance computing and is focused on theory and applications of Deep Learning. Justin develops deep learning models for large financial datasets such as: high-frequency data from limit order books, loans, and options. He is also developing deep learning methods for constructing partial differential equation models from data. Justin received his PhD from Stanford University and holds a Bachelors degree from Princeton University. He was a Chapman Fellow at the Department of Mathematics at Imperial College. He was awarded the 2014 SIAM Financial Mathematics and Engineering Conference Paper Prize.
Editorial positions: Associate Editor for Mathematical Finance, Managing Editor for Quantitative Finance, Associate Editor for Special Issue of Management Science on Data Science.
Computational grants: 44 million core hours on Blue Waters supercomputer (2016-2020) and 120,000 GPU hours on the Summit supercomputer (2020-2021).
Seminars and invited presentations in Year 2020: Machine Learning in Finance conference at the University of Oxford (September 25); NSF Workshop on Machine Learning in Dallas; Dept. of Applied Mathematics at Brown University; Two Sigma Investments in New York City; Dept. of Mathematics at the University of Minnesota.
Deep Learning, Machine Learning, Machine Learning Models of Financial Data, Financial Mathematics
Major / recent publications:
- "Mean Field Analysis of Neural Networks: A Law of Large Numbers" (with K. Spiliopoulos). SIAM Journal on Applied Mathematics, to appear 2020.
- "Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem" (with K. Spiliopoulos). Stochastic Systems, to appear 2020.
- "Inference for large financial systems" (with G. Schwenkler and K. Giesecke). Mathematical Finance, 2020.
- "Mean Field Analysis of Deep Neural Networks" (with K. Spiliopoulos). Mathematics of Operations Research, to appear 2020. arXiv: 1903.04440, 2020.
- "Universal features of price formation in financial markets: perspectives from Deep Learning" (with Rama Cont). Quantitative Finance, 2019.
- "Mean Field Analysis of Neural Networks: A Central Limit Theorem" (with K. Spiliopoulos). Stochastic Processes and their Applications, 2019.