Research Interests
Justin Sirignano is currently an Assistant Professor at the University of Illinois at UrbanaChampaign. Previously, he was a Chapman Fellow at the Department of Mathematics, Imperial College London (20152016) and received his PhD from Stanford University (20102015). Justin received his BSE from Princeton University (20062010).
Justin Sirignano's research interests include machine learning and applied probability. Justin's research group and collaborators work on a range of applications in engineering and mathematical finance. Justin Sirignano's research won the 2014 SIAM Financial Mathematics and Engineering Conference Paper Prize.
Examples of current projects using machine learning are: reducedorder models in engineering, solution of highdimensional partial differential equations, models of high frequency financial data, and asymptotic analysis of neural networks using weak convergence analysis for stochastic processes.
Courses
 Introduction to Machine Learning (Imperial College Math Dept., Spring 2016): Master/PhD level course on machine learning.
 Deep Learning (University of Illinois at UrbanaChampaign): 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 UrbanaChampaign): Deep learning applications in (1) reinforcement learning, (2) image recognition, and (3) highfrequency models of financial markets. There will be a special focus on distributed training of deep learning models. Supported by a computational grant from Microsoft.
Preprints
Publications
 "Statistical Inference for Large Systems" (with G. Schwenkler and K. Giesecke). Mathematical Finance, forthcoming 2019.
 "DGM: A deep learning algorithm for solving partial differential equations" (with K. Spiliopoulos). Journal of Computational Physics, forthcoming 2018.
 "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): 23222362, 2014.
 "Largescale 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 SecondaryAir Addition in a Continuous OneDimensional Spray Combustor" (with L. Rodriquez, A. Sideris, and W. Sirignano). Journal of Propulsion and Power, 2010.
 "A ForwardBackward 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.
Computational Grants
 Blue Waters supercomputer, 505,000 node hours, value of $320,000.
Student Researchers
 Lei Fan, Xiaobo Dong, Yunxiang Zhang, Giri Tarun, and Rachneet Kaur.
Presentations to Industry
 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 ManagementImperial Workshop, London, September 2015. Risk Analysis for Loan Portfolios.
 Lending Club, San Francisco, June 2015. Risk Analysis for Loan Portfolios.
Conference Organization and Professional Service
 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, 2018.
 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.
 Largescale Portfolio Risk session at INFORMS Annual Meeting, Philadelphia, November 2015.
Seminar and Conference Presentations
 Department of Mathematics, University of Michigan, April 2019.
 Department of Mathematics, Oxford University, April 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, April 2019.
 American Institute of Mathematics/NSF Workshop on "Deep Learning and PDEs", 2019.
 Department of Statistics, Purdue University, 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 Largescale Portfolio Risk Session.
 Finance and Stochastics Seminar at Imperial College, London, October 2015.
 LondonParis Bachelier Workshop on Mathematical Finance, London, September 2015. Invited Speaker.
 IPAM Workshop on Systemic Risk and Financial Networks, Los
Angeles, 2015.
 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.
