Status:
Personal website:
+44 1865 270503
Research groups:
Address
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
Research interests:
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.
Preferred address:
E-Mail: Justin.Sirignano@maths.ox.ac.uk
Further details:
Complete list of publications and presentations.
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.
Teaching:
Applied Mathematics, Deep Learning, Machine Learning, Stochastic Models, Financial Mathematics
Major / recent publications:
- "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.