Mickaël Binois

912 total citations
29 papers, 492 citations indexed

About

Mickaël Binois is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Mickaël Binois has authored 29 papers receiving a total of 492 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 14 papers in Computational Theory and Mathematics and 11 papers in Management Science and Operations Research. Recurrent topics in Mickaël Binois's work include Advanced Multi-Objective Optimization Algorithms (14 papers), Gaussian Processes and Bayesian Inference (11 papers) and Probabilistic and Robust Engineering Design (8 papers). Mickaël Binois is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (14 papers), Gaussian Processes and Bayesian Inference (11 papers) and Probabilistic and Robust Engineering Design (8 papers). Mickaël Binois collaborates with scholars based in France, United States and Switzerland. Mickaël Binois's co-authors include Robert B. Gramacy, Michael Ludkovski, Olivier Roustant, David Ginsbourger, Victor Picheny, Justin M. Wozniak, Nicholson Collier, Jonathan Ozik, Charles M. Macal and Stéphane Lanteri and has published in prestigious journals such as Technometrics, European Journal of Operational Research and Optics Express.

In The Last Decade

Mickaël Binois

27 papers receiving 486 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mickaël Binois France 11 198 163 145 98 50 29 492
Loïc Le Gratiet France 10 284 1.4× 117 0.7× 107 0.7× 278 2.8× 38 0.8× 13 649
Kuang Zhou China 13 62 0.3× 278 1.7× 78 0.5× 106 1.1× 49 1.0× 34 588
D. Finkel United States 7 155 0.8× 117 0.7× 38 0.3× 29 0.3× 77 1.5× 8 403
Rui Tuo United States 13 246 1.2× 137 0.8× 122 0.8× 215 2.2× 79 1.6× 33 519
Genetha A. Gray United States 8 187 0.9× 135 0.8× 51 0.4× 46 0.5× 33 0.7× 24 405
Joerg Gablonsky United States 9 205 1.0× 132 0.8× 44 0.3× 39 0.4× 62 1.2× 13 502
Shan Ba United States 9 261 1.3× 108 0.7× 200 1.4× 168 1.7× 41 0.8× 15 524
T. J. Sullivan United Kingdom 11 55 0.3× 77 0.5× 18 0.1× 139 1.4× 46 0.9× 24 672
Xiaoqun Wang China 16 120 0.6× 34 0.2× 45 0.3× 332 3.4× 65 1.3× 62 943
Andrey Pepelyshev Germany 19 393 2.0× 71 0.4× 482 3.3× 197 2.0× 49 1.0× 68 790

Countries citing papers authored by Mickaël Binois

Since Specialization
Citations

This map shows the geographic impact of Mickaël Binois'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 Mickaël Binois with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mickaël Binois more than expected).

Fields of papers citing papers by Mickaël Binois

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mickaël Binois. 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 Mickaël Binois. The network helps show where Mickaël Binois may publish in the future.

Co-authorship network of co-authors of Mickaël Binois

This figure shows the co-authorship network connecting the top 25 collaborators of Mickaël Binois. A scholar is included among the top collaborators of Mickaël Binois 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 Mickaël Binois. Mickaël Binois 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.
Kerr, Cliff C., et al.. (2025). Improving policy-oriented agent-based modeling with history matching: A case study. Epidemics. 52. 100845–100845. 1 indexed citations
2.
Binois, Mickaël, et al.. (2025). hetGPy: Heteroskedastic Gaussian Process Modeling in Python. The Journal of Open Source Software. 10(106). 7518–7518. 1 indexed citations
3.
Binois, Mickaël, et al.. (2025). Traffic prediction by combining macroscopic models and Gaussian processes. Applied Mathematical Modelling. 150. 116397–116397.
4.
Binois, Mickaël, et al.. (2023). Validation of Calibration Strategies for Macroscopic Traffic Flow Models on Synthetic Data. 1–6. 1 indexed citations
5.
Collier, Nicholson, Justin M. Wozniak, Yadu Babuji, et al.. (2023). Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis. PubMed. 2023. 868–877. 7 indexed citations
6.
Duvigneau, Régis, et al.. (2022). Geometrically consistent aerodynamic optimization using an isogeometric Discontinuous Galerkin method. Computers & Mathematics with Applications. 128. 368–381. 1 indexed citations
7.
Binois, Mickaël, et al.. (2022). Data-driven uncertainty quantification in macroscopic traffic flow models. Advances in Computational Mathematics. 48(6). 9 indexed citations
8.
Binois, Mickaël, et al.. (2022). Sensitivity Prewarping for Local Surrogate Modeling. Technometrics. 64(4). 535–547. 5 indexed citations
9.
Binois, Mickaël, et al.. (2022). A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization. arXiv (Cornell University). 2(2). 1–26. 74 indexed citations
10.
Binois, Mickaël, et al.. (2022). Advanced Numerical Modeling Methods for the Characterization and Optimization of Metasurfaces. SPIRE - Sciences Po Institutional REpository. 1–4.
11.
Dumonteil, Éric, et al.. (2022). Simulation and design of an IPHI-based neutron source, first steps toward SONATE. Journal of Neutron Research. 24(3-4). 337–345. 1 indexed citations
12.
Ozik, Jonathan, Justin M. Wozniak, Nicholson Collier, Charles M. Macal, & Mickaël Binois. (2021). A population data-driven workflow for COVID-19 modeling and learning. The International Journal of High Performance Computing Applications. 35(5). 483–499. 34 indexed citations
13.
Elsawy, Mahmoud, Mickaël Binois, Régis Duvigneau, et al.. (2021). Multiobjective Statistical Learning Optimization of RGB Metalens. ACS Photonics. 8(8). 2498–2508. 28 indexed citations
14.
Elsawy, Mahmoud, Mickaël Binois, Régis Duvigneau, Stéphane Lanteri, & Patrice Genevet. (2021). Optimization of metasurfaces under geometrical uncertainty using statistical learning. Optics Express. 29(19). 29887–29887. 12 indexed citations
15.
Binois, Mickaël, Victor Picheny, Patrick Taillandier, & Abderrahmane Habbal. (2020). The Kalai-Smorodinsky solution for many-objective Bayesian optimization. Journal of Machine Learning Research. 21(150). 1–42. 3 indexed citations
16.
Gramacy, Robert B., et al.. (2020). On‐site surrogates for large‐scale calibration. Applied Stochastic Models in Business and Industry. 36(2). 283–304. 8 indexed citations
17.
Chung, Matthias, et al.. (2019). Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes. SIAM Journal on Scientific Computing. 41(4). A2212–A2238. 10 indexed citations
18.
Binois, Mickaël, David Ginsbourger, & Olivier Roustant. (2017). On the choice of the low-dimensional domain for global optimization via random embeddings. arXiv (Cornell University). 27 indexed citations
19.
Binois, Mickaël, Didier Rullière, & Olivier Roustant. (2015). On the estimation of Pareto fronts from the point of view of copula theory. Information Sciences. 324. 270–285. 8 indexed citations
20.
Binois, Mickaël, David Ginsbourger, & Olivier Roustant. (2014). Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. European Journal of Operational Research. 243(2). 386–394. 35 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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