中文
Nicolas LANGRENÉ
Associate Professor
Email:
nicolaslangrene@uic.edu.cn
Office:
T3-602-R19
Office Tel:
+86 (0)756 3677225
Timetable:
timetable
Main Appointment:
Financial Mathematics
Research Area:
computational statistics, quantitative finance, stochastic control, deep learning
Education:
Ph.D. Université Paris Cité M.Sc. Université Paris Cité M.Sc. Université Grenoble Alpes M.Sc.Eng. Grenoble INP ENSIMAG
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Biography

Dr. Nicolas Langrené is an associate professor in the UIC Financial Mathematics Programme. He received his Masters of Science in Computer Science and Applied Mathematics from Grenoble INP ENSIMAG and Université Grenoble Alpes (formerly Joseph Fourier University) in 2009, and his Master of Science in Financial Mathematics and his PhD in Applied Mathematics from the Université Paris Cité (formerly Paris Diderot University) in 2010 and 2014 respectively. He then worked at the CSIRO (Australian national research centre) before joining UIC in 2022. His research interests include quantitative and computational finance, stochastic control and stochastic optimization, machine learning and deep learning, computational statistics and data visualization.

Publications
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  • 1.

    Designing higher value roads to preserve species at risk by optimally controlling traffic flow; N Davey, N Langrené, W Chen, J Rhodes, S Dunstall, S Halgamuge; Annals of Operations Research 320(2) 663-693(2023)

  • 2.

    Closed-form approximations with respect to the mixing solution for option pricing under stochastic volatility; K Das, N Langrené; Stochastics 94(5) 745-788(2022)

  • 3. Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications; A Bachouch, C Huré, N Langrené, H Pham; Methodology and Computing in Applied Probability 24(1) 143-178 (2022)
  • 4.

    Portfolio optimization with a prescribed terminal wealth distribution; I Guo, N Langrené, G Loeper, W Ning; Quantitative Finance 22(2) 333-347 (2022)

  • 5. Robust utility maximization under model uncertainty via a penalization approach; I Guo, N Langrené, G Loeper, W Ning; Mathematics and Financial Economics 16(1) 51-88 (2022)
  • 6. Visual diagnostics for constrained optimisation with application to guided tours; HS Zhang, D Cook, U Laa, N Langrené, P Menéndez; R Journal 13(2) 624-641 (2021)
  • 7. Using a stochastic economic scenario generator to analyse uncertain superannuation and retirement outcomes; W Chen, B Koo, Y Wang, C O'Hare, N Langrené, P Toscas, Z Zhu; Annals of Actuarial Science 15(3) 549-566 (2021)
  • 8. Fast multivariate empirical cumulative distribution function with connection to kernel density estimation; N Langrené, X Warin; Computational Statistics and Data Analysis 162 107267 (2021)
  • 9. Markovian approximation of the rough Bergomi model for Monte Carlo option pricing; Q Zhu, G Loeper, W Chen, N Langrené; Mathematics 9(5) 528 (2021)
  • 10. Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis; C Huré, H Pham, A Bachouch, N Langrené; SIAM Journal on Numerical Analysis 59(1) 525–557 (2021)

Teaching

DS4023. Machine Learning (Y4, 2023 Spring)
OR3023. Simulation (Y4, 2023 Spring)
FINM4053. Numerical and Simulation Techniques in Finance (Y4, 2022 Fall)
COMP3153. C++ Programming Language (Y2, 2022 Fall)
DS1023. Advanced Mathematics for Data Science (Y1, 2022 Spring)

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