Research Fellow; Associate Professor of Mathematical and Computational Finance
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, which has a variety of applications in science, engineering, and finance.
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.
“Mean Field Analysis of Neural Networks: A Law of Large Numbers” (with K. Spiliopoulos). SIAM Journal on Applied Mathematics, 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, 2021. 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.
“PDE-constrained Models with Neural Network Terms: Optimization and Global Convergence” (with J. MacArt and K. Spiliopoulos). arXiv:2105.08633, 2021.