Prof. Justin Sirignano
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Office of Naval Research (ONR): multi-university $1 million grant in collaboration with Jonathan MacArt (Notre Dame) and Marco Panesi (UIUC)
Co-PI on $16.5 million DoE Center for Exascale-enabled Scramjet Design at UIUC (2020-2021).
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 Years 2020-2022: Google DeepMind, Two Sigma Investments, Isaac Newton Institute at Univ. of Cambridge, NSF Workshop on Machine Learning in Dallas, Brown University Dept. of Applied Mathematics, London Business School, UCLA Dept. of Mathematics, SIAM Conf. on Financial Math, and SIAM Conf. on Dynamical Systems.
Faculty Member in the Center for Hypersonics and Entry Systems Studies (CHESS)
Faculty Member in the Center for Mathematics of Random Systems
- "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, 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.
- "Global Convergence of the ODE Limit for Online Actor-Critic Algorithms in Reinforcement Learning" (with Z. Wang). Invited Revision at Stochastic Systems. arXiv:2108.08655, 2021.
- "Deep Learning Closure of the Navier-Stokes Equations for Transitional Flows" (with J. F. MacArt and M. Panesi). Proceedings of AIAA Scitech, 2022.
- "Continuous-time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equations" (with Ziheng Wang). Invited submission to Special Issue of Mathematical Finance. arXiv: 2202.06637, 2022.
- "Online Adjoint Methods for the Optimization of PDEs" (with K. Spiliopoulos). Applied Mathematics and Optimization, In Press, 2022.
- "DPM: A deep learning PDE augmentation method with application to large-eddy simulation" (with J. F. MacArt and J. B. Freund). Journal of Computational Physics, 2020.
- "Embedded training of neural-network subgrid-scale turbulence models" (with J. F. MacArt and J. B. Freund). Physical Review Fluids, 2021.
- "PDE-constrained Models with Neural Network Terms: Optimization and Global Convergence" (with J. F. MacArt and K. Spiliopoulos). arXiv:2105.08633, Major Revision at Journal of Computational Physics, 2021.
- "Neural Q-learning for solving Elliptic PDEs" (with S. Cohen and D. Jiang). https://arxiv.org/abs/2203.17128, 2022.
Courses: Deep Learning, Machine Learning, Partial Differential Equations, Stochastic Models, Financial Mathematics
Course director of the Oxford Masters program in Mathematical & Computational Finance
Career Placement of PhD Students: Lei Fan (J.P. Morgan Systematic Trading), Xiaobo Dong (J.P. Morgan Machine Learning)
Ranked Excellent in Teaching for a graduate course on deep learning with two hundred students.
Justin is an Associate Professor of Mathematics at the University of Oxford whose research is at the intersection of Applied Mathematics, Machine Learning, and High Performance Computing. His recent research has focused on the mathematical 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 (PDE) models from data, which has a variety of applications in science, engineering, and finance. This includes recent work on developing deep learning-based PDE models as reduced-order simulations for "computationally-challenging physics" involving turbulent flows, whose accurate modeling is critical for flight vehicle design.
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.
A fully-funded PhD position is currently available on the topic of developing machine learning-based PDE models. Please email me if interested.