Justin Sirignano is currently an Assistant Professor at the University of Illinois at Urbana-Champaign with appointments in the Department of Industrial & Systems Engineering and the Coordinated Science Lab, where he is a member of the Parallel Computing group.
Previously, he was a Chapman Fellow at the Department of Mathematics, Imperial College London (2015-2016) and received his PhD from Stanford University (2010-2015). Justin received his BSE from Princeton University (2006-2010). Justin Sirignano's research won the 2014 SIAM Financial Mathematics and Engineering Conference Paper Prize.
Justin's research lies at the intersection of applied mathematics, machine learning, and high-performance computing. Our research group is currently working on a range of problems in mathematical finance and engineering.
Current research projects include:
- Deep learning models of financial data
- Solution of high-dimensional PDEs
- Asymptotic analysis of neural networks
- Deep learning for scientific and engineering applications
There is an opening for a 2-year postdoc position as well as RA positions. Researchers will benefit from broad collaborations with engineers, computer scientists, and applied mathematicians. They will have access to state-of-the-art super-computing and GPU resources. Projects will leverage massive datasets (petabytes) for both financial and engineering applications.
- Introduction to Machine Learning (Imperial College Math Dept., Spring 2016): Master/PhD level course on machine learning.
- Deep Learning (University of Illinois at Urbana-Champaign): Master/PhD level course on deep learning. Topics include convolution neural networks, recurrent neural networks, and deep reinforcement learning. Homeworks and projects on image classification, video recognition, and deep reinforcement learning. Training of deep learning models using TensorFlow and GPUs. Supported by a computational grant from a national supercomputer. Course website
- Deep Learning II (University of Illinois at Urbana-Champaign): Deep learning applications in (1) reinforcement learning, (2) image recognition, and (3) high-frequency models of financial markets. There will be a special focus on distributed training of deep learning models. Supported by a computational grant from Microsoft.
- "DGM: A deep learning algorithm for solving partial differential equations" (with K. Spiliopoulos). Journal of Computational Physics, 2018.
- "Mean Field
Analysis of Neural Networks: A Central Limit Theorem" (with K. Spiliopoulos). Stochastic Processes and their Applications, forthcoming 2019.
- "Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem" (with K. Spiliopoulos). Stochastic Systems, forthcoming 2019.
- "Statistical Inference for Large Systems" (with G. Schwenkler and K. Giesecke). Mathematical Finance, forthcoming 2019.
- "Universal features of price formation in financial markets: perspectives from Deep Learning"
(with Rama Cont). Media coverage: Risk Magazine. Quantitative Finance, forthcoming 2019.
- "Stochastic Gradient Descent in Continuous Time" (with K. Spiliopoulos). SIAM Journal on Financial Mathematics,
8(1), 933–961, 2017.
- "Deep Learning for Limit Order Books". Quantitative Finance, forthcoming 2018.
- "Risk Analysis for Large Pools of Loans" (with K. Giesecke). Winner of SIAM
Financial Mathematics and Engineering Conference Paper Prize. Management Science, 2018.
- "Deep Learning Models in Finance". Invited article for SIAM News, June 2017.
- "Fluctuation Analysis for the Loss from
Default" (with K. Giesecke and K. Spiliopoulos).
Stochastic Processes and their Applications, (124): 2322-2362, 2014.
- "Large-scale Loan Portfolio Selection" (with K. Giesecke and
G. Tsoukalas). Operations Research, 2016.
- "Large Portfolio Asymptotics for Loss From
Default" (with K. Giesecke, K. Spiliopoulos, and R. Sowers).
Mathematical Finance, 2015.
- "Optimization of Secondary-Air Addition in a Continuous One-Dimensional Spray Combustor" (with L. Rodriquez, A. Sideris, and W. Sirignano). Journal of Propulsion and Power, 2010.
- "A Forward-Backward Algorithm for Stochastic Control Problems" (with S. Ludwig, R. Huang, and G. Papanicolaou).
Proceedings of the First International Conference on Operations Research and Enterprise Systems. Vilamoura, Portugal.
- Blue Waters supercomputer, 605,000 node hours, value of $383,000.
Presentations to Industry
- Lei Fan, Xiaobo Dong, Yunxiang Zhang, Giri Tarun, and Rachneet Kaur.
Conference Organization and Professional Service
- J.P. Morgan, New York City, August 2017. Deep Learning in Finance.
- Bank of England, London, May 2016. Machine Learning for Loan Risk.
- Winton Capital Management, London, January 2016. Modeling financial data with Neural Networks.
- Capital Fund Management-Imperial Workshop, London, September 2015. Risk Analysis for Loan Portfolios.
- Lending Club, San Francisco, June 2015. Risk Analysis for Loan Portfolios.
Seminar and Conference Presentations
- Co-organizer of SIAM Workshop on Mathematics of Deep Learning.
- Managing Editor, Quantitative Finance.
- Associate Editor, Journal of Dynamics and Games (an AIMS journal).
- Associate Editor, Special Issue of Management Science on Data Science.
- Machine Learning minisymposium at SIAM Financial Mathematics Conference, Toronto, 2019.
- Machine Learning for Finance minisymposium at SIAM Financial Mathematics Conference, Austin, November 2016.
- Machine learning in Finance session at INFORMS Annual Meeting, Houston, October 2017.
- Financial engineering session at INFORMS Applied Probability Meeting, Northwestern University, July 2017.
- Machine Learning for Finance session at INFORMS Annual Meeting, Nashville, November 2016.
- Large-scale Portfolio Risk session at INFORMS Annual Meeting, Philadelphia, November 2015.
- Reviewer for SIAM Financial Math, Journal of Machine Learning Research, Constructive Approximation (Special Issue on Deep Learning), NeurIPS, Operations Research, Stochastic Systems, and Management Science.
- Workshop on Machine Learning in Finance at the University of Toronto, 2019. Invited Talk.
- SIAM Conference on Analysis of Partial Differential Equations, La Quinta CA, 2019. Invited Talk.
- Department of Mathematics, UCLA, Colloquium, 2019. Deep Learning: Applications and Asymptotics.
- Department of Mathematics, University of Michigan, 2019.
- Department of Mathematics, Oxford University, 2019. Mean Field Analysis of Neural Networks in Machine Learning.
- Department of Statistics, Carnegie Mellon University, January 2019.
- Department of Industrial Engineering & Operations Research, Columbia University, February 2019.
- Department of Management Science & Engineering, Stanford University, 2019.
- Department of Mathematics, Washington University in St. Louis, 2019.
- BIRS Workshop on Modern Challenges between Financial Mathematics, Financial Technology, and Financial Economics. Banff, Canada, 2020. Invited Talk.
- American Institute of Mathematics/NSF Workshop on "Deep Learning and PDEs", 2019.
- Department of Statistics, Purdue University, 2019.
- SIAM Conference on Financial Math, Toronto, June 2019.
- INFORMS Annual Meeting, Seattle, October 2019.
- Department of Mathematics, Univ. of Illinois Urbana Champaign, Sept. 2018. Mean Field Analysis of Neural Networks in Machine Learning.
- SIAM Annual Conference, July 2018.
- London Quantitative Finance Seminar, May 2018. Deep Learning Models of High Frequency Financial Data.
- Seminar at the Department of Applied Math at the University of Colorado Boulder, April 2018.
- Deep Learning conference at the National Center for Supercomputing Applications, 2017. Deep Learning for PDEs.
- Seminar at Princeton University, 2017. Machine Learning in Finance.
- J.P. Morgan, Machine Learning and AI Forum seminar, August 2017. Deep Learning in Finance.
- Seminar at Northwestern University, April 2017.
- Seminar at UIUC Machine Learning Seminar Series, March 2017.
- Seminar at UIUC Business School, February 2017. Deep Learning for Modeling Limit Order Books.
- SIAM Financial Mathematics Conference, Austin, Texas, November 2016.
- Seminar at London Business School, London, June 2016. Deep Learning for Modeling Limit Order Books.
- Seminar at Oxford University, May 2016. Deep Learning for Modeling Financial Data.
- Statistics Seminar at Imperial College, London, May 2016. Deep Learning for Modeling Financial Data.
- INFORMS Annual Meeting, Philadelphia, November 2015. Invited Speaker and Organizer of Large-scale Portfolio Risk Session.
- Finance and Stochastics Seminar at Imperial College, London, October 2015.
- London-Paris Bachelier Workshop on Mathematical Finance, London, September 2015. Invited Speaker.
- IPAM Workshop on Systemic Risk and Financial Networks, Los
- SIAM Financial Mathematics and Engineering Meeting, Chicago,
2014. Invited Speaker.
- INFORMS Annual Meeting, San Francisco, 2014. Invited Speaker
and Organizer of Financial Risks Session.
- Likelihood Estimation for Large Financial Systems. Joint Mathematics Meeting, Baltimore, 2014. Invited Speaker.
- Fluctuation Analysis
for Loss from Default. INFORMS
Annual Meeting, Minneapolis, 2013. Invited Speaker.
- Lecture on Subprime Crisis. For a general audience at Stanford University, 2013.
- Fifth Western Conference on Mathematical Finance, Stanford University, 2013. Invited Speaker.
- INFORMS Annual Meeting, Phoenix, October, 2012. Invited Speaker.
- Financial Mathematics Seminar, Stanford University, 2012. Invited Speaker.
- SIAM Financial Mathematics and Engineering Meeting, Minneapolis, 2012. Chair of Credit Risk Session.
- Annual Meeting of the Canadian Applied and Industrial Mathematics Society, Toronto, 2012. Invited Speaker.
- 5th Financial Risks International Forum, Paris, France, 2012.