Justin Sirignano's research interests include machine learning, applied probability, stochastic modeling, and finance. Justin Sirignano is currently an Assistant Professor (tenure track) at the University of Illinois at Urbana-Champaign. 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).
Current research interests include a range of applications in finance and engineering. Justin Sirignano's research won the 2014 SIAM Financial Mathematics and Engineering Conference Paper Prize.
- 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, Fall 2016 and 2017): 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.
- Deep Learning II (University of Illinois at Urbana-Champaign, Spring 2017): Deep learning applications in (1) reinforcement learning, (2) image recognition, and (3) high-frequency models of financial markets. The course will provide an introduction to distributed training of neural networks.
- "Stochastic Gradient Descent in Continuous Time" (with K. Spiliopoulos). SIAM Journal on Financial Mathematics, forthcoming.
- "Risk Analysis for Large Pools of Loans" (with K. Giesecke). Winner of SIAM
Financial Mathematics and Engineering Conference Paper Prize. Management Science, forthcoming.
- "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.
Presentations to Industry
- Blue Waters supercomputer, 322,000 node hours, estimated value $200,000.
Organized Minisymposiums and Sessions
- 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.
Conference and Seminar Presentations
- 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 minisymposium at SIAM Financial Mathematics Conference, Austin, November 2016.
- Machine Learning for Finance session at INFORMS Annual Meeting, Nashville, November 2016.
- Large-scale Portfolio Risk session at INFORMS Annual Meeting, Philadelphia, November 2015.
- Seminar at the Department of Applied Math at the University of Colorado Boulder, 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. Stochastic Gradient Descent in Continuous Time
- 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.
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.