Nicolas Langrené

982 total citations · 1 hit paper
29 papers, 462 citations indexed

About

Nicolas Langrené is a scholar working on Finance, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, Nicolas Langrené has authored 29 papers receiving a total of 462 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Finance, 7 papers in Management Science and Operations Research and 6 papers in Economics and Econometrics. Recurrent topics in Nicolas Langrené's work include Stochastic processes and financial applications (12 papers), Capital Investment and Risk Analysis (5 papers) and Risk and Portfolio Optimization (4 papers). Nicolas Langrené is often cited by papers focused on Stochastic processes and financial applications (12 papers), Capital Investment and Risk Analysis (5 papers) and Risk and Portfolio Optimization (4 papers). Nicolas Langrené collaborates with scholars based in Australia, France and China. Nicolas Langrené's co-authors include Huyên Pham, Shengxin Zhu, Zhaofeng Zhang, Xavier Warin, Idris Kharroubi, Côme Huré, René Aïd, Luciano Campi, Wim van Ackooij and Chen Wen and has published in prestigious journals such as IEEE Transactions on Power Systems, SIAM Journal on Numerical Analysis and Journal of Statistical Software.

In The Last Decade

Nicolas Langrené

29 papers receiving 449 citations

Hit Papers

Unleashing the potential of prompt engineering for large ... 2025 2026 2025 10 20 30 40
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Unleashing the potential of prompt engineering for large language models
    2025 43 citations Zhaofeng Zhang, Nicolas Langrené et al. Patterns profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nicolas Langrené Australia 12 148 99 78 71 54 29 462
Pavlo Krokhmal United States 13 136 0.9× 37 0.4× 41 0.5× 246 3.5× 78 1.4× 39 505
Laurent Callot Denmark 8 92 0.6× 63 0.6× 78 1.0× 187 2.6× 115 2.1× 18 450
Mogens Bladt Denmark 16 184 1.2× 110 1.1× 29 0.4× 205 2.9× 70 1.3× 42 686
Tomáš Tichý Czechia 11 201 1.4× 59 0.6× 56 0.7× 176 2.5× 116 2.1× 83 502
Christian Schittenkopf Austria 10 148 1.0× 115 1.2× 46 0.6× 129 1.8× 186 3.4× 22 445
Pavlo A. Krokhmal United States 5 259 1.8× 25 0.3× 79 1.0× 313 4.4× 180 3.3× 9 586
J. S. Dagpunar United Kingdom 14 45 0.3× 98 1.0× 22 0.3× 52 0.7× 48 0.9× 40 602
Haeran Cho United Kingdom 10 63 0.4× 101 1.0× 65 0.8× 50 0.7× 92 1.7× 24 438
Olaf Korn Germany 12 359 2.4× 121 1.2× 52 0.7× 162 2.3× 291 5.4× 47 720
Viktor Winschel Switzerland 7 38 0.3× 52 0.5× 18 0.2× 35 0.5× 94 1.7× 8 365

Countries citing papers authored by Nicolas Langrené

Since Specialization
Citations

This map shows the geographic impact of Nicolas Langrené's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nicolas Langrené with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Langrené more than expected).

Fields of papers citing papers by Nicolas Langrené

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicolas Langrené. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicolas Langrené. The network helps show where Nicolas Langrené may publish in the future.

Co-authorship network of co-authors of Nicolas Langrené

This figure shows the co-authorship network connecting the top 25 collaborators of Nicolas Langrené. A scholar is included among the top collaborators of Nicolas Langrené based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nicolas Langrené. Nicolas Langrené is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Zhaofeng, et al.. (2025). Unleashing the potential of prompt engineering for large language models. Patterns. 6(6). 101260–101260. 43 indexed citations breakdown →
2.
Cook, Dianne, et al.. (2024). cubble: An R Package for Organizing and Wrangling Multivariate Spatio-Temporal Data. Journal of Statistical Software. 110(7). 2 indexed citations
3.
Cook, Dianne, et al.. (2024). A Tidy Framework and Infrastructure to Systematically Assemble Spatio-temporal Indexes from Multivariate Data. Journal of Computational and Graphical Statistics. 34(2). 642–653. 1 indexed citations
4.
Zhang, Zhaofeng, et al.. (2023). Unleashing the potential of prompt engineering for large language models. arXiv (Cornell University). 72 indexed citations
5.
Tack, Guido, et al.. (2021). Versatile and Robust Transient Stability Assessment via Instance Transfer Learning. 2021 IEEE Power & Energy Society General Meeting (PESGM). 1–5. 3 indexed citations
6.
Wen, Chen, et al.. (2020). Using a stochastic economic scenario generator to analyse uncertain superannuation and retirement outcomes. Annals of Actuarial Science. 15(3). 549–566. 4 indexed citations
7.
Langrené, Nicolas, et al.. (2019). Robust utility maximization under model uncertainty via a penalization approach. arXiv (Cornell University). 1 indexed citations
8.
Huré, Côme, et al.. (2018). Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications. arXiv (Cornell University). 37 indexed citations
9.
Langrené, Nicolas & Xavier Warin. (2018). Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating. Journal of Computational and Graphical Statistics. 28(3). 596–608. 29 indexed citations
10.
Langrené, Nicolas, et al.. (2018). Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach. Quantitative Finance. 19(3). 519–532. 11 indexed citations
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12.
Langrené, Nicolas, et al.. (2017). Field exploration: when to start extracting?. 1 indexed citations
13.
Langrené, Nicolas, et al.. (2017). Sharp Target Range Strategy for Multiperiod Portfolio Choice by Decensored Least Squares Monte Carlo. arXiv (Cornell University). 2 indexed citations
14.
Langrené, Nicolas, et al.. (2016). SWITCHING TO NONAFFINE STOCHASTIC VOLATILITY: A CLOSED-FORM EXPANSION FOR THE INVERSE GAMMA MODEL. International Journal of Theoretical and Applied Finance. 19(5). 1650031–1650031. 2 indexed citations
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Langrené, Nicolas, et al.. (2015). Switching to Non-Affine Stochastic Volatility: A Closed-Form Expansion for the Inverse Gamma Model. SSRN Electronic Journal. 2 indexed citations
18.
Kharroubi, Idris, Nicolas Langrené, & Huyên Pham. (2015). Discrete time approximation of fully nonlinear HJB equations via BSDEs with nonpositive jumps. The Annals of Applied Probability. 25(4). 23 indexed citations
19.
Kharroubi, Idris, Nicolas Langrené, & Huyên Pham. (2014). A numerical algorithm for fully nonlinear HJB equations:An approach by control randomization. Monte Carlo Methods and Applications. 20(2). 145–165. 42 indexed citations
20.
Aïd, René, Luciano Campi, & Nicolas Langrené. (2012). A STRUCTURAL RISK‐NEUTRAL MODEL FOR PRICING AND HEDGING POWER DERIVATIVES. Mathematical Finance. 23(3). 387–438. 36 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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