Justin Sirignano
Deep Reinforcement Learning Models for High-Frequency Data
(bbbc)Jan 2021 - Dec 2021
IE 498: Deep Learning
(bbby)Jan 2020 - May 2020
Deep Learning
(bayw)Sep 2019 - Dec 2019
Deep Learning
(bawc)Feb 2019 - May 2019
Deep Learning
(bauh)Sep 2018 - Dec 2018
Deep Learning II
(bapo)Jan 2018 - Apr 2018
Deep Learning for Finance and Engineering Applications
(bahp)Dec 2017 - Dec 2019
IE 598: Deep Learning (Fall 2017)
(banb)Sep 2017 - Dec 2017
Deep Learning
(balq)Sep 2017 - Dec 2017
Deep Learning: Modeling Financial Data and Reinforcement Learning
(baex)Jun 2016 - Dec 2016
Deep Learning with GPUs
(bacu)Apr 2016 - Dec 2016
2020
Justin Sirignano, Jonathan F. MacArt, and Jonathan B. Freund (2020): DPM: A deep learning PDE augmentation method with application to large-eddy simulation, Journal of Computational Physics, Elsevier Inc., Vol 423, pp109811
2019
Justin Sirignano and Rama Cont (2019): Universal Features of Price Formation in Financial Markets: Perspectives from Deep Learning, Quantitative Finance, Routledge, Vol 19, Num 9, pp1449-1459
2018
Justin Sirignano and Konstantinos Spiliopoulos (2018): DGM: A Deep Learning Algorithm for Solving Partial Differential Equations, Journal of Computational Physics, Elsevier BV, Vol 375, pp1339-1364
2019
Jonathan Freund, Justin Sirignano, Jonathan MacArt (2019): Machine-Learning Turbulence Models for Simulations of Turbulent Combustion, 2019 Blue Waters Annual Report, pp148-149
Justin Sirignano, Jonathan B. Freund, Jonathan F. MacArt (2019): HPC Development of Deep Learning Models in Scientific Computing and Finance, 2019 Blue Waters Annual Report, pp234
2018
Justin Sirignano (2018): Topology-Aware Distributed Graph Processing for Tightly Coupled Clusters, 2018 Blue Waters Annual Report, pp187
Blue Waters Illinois allocations awarded to 26 research teams
Mar 7, 2017
Twenty-six research teams at the University of Illinois at Urbana-Champaign have been allocated computation time on the National Center for Supercomputing Application's (NCSA) sustained-petascale Blue Waters supercomputer after applying in Fall 2016. These allocations range from 25,000 to 600,000 node-hours of compute time over a time span of either six months or one year. The research pursuits of these teams are incredibly diverse, ranging anywhere from physics to political science.
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