Flowers, A. R., Franck, C. T., Binois, M., Park, C. & Gramacy, R. B. (2026). Modular jump Gaussian processes. Data Science in Science, 5(1), 2612651. Article Preprint
Würth, A., Binois, M. & Goatin, P. (2026). Traffic prediction by combining macroscopic models and Gaussian processes. Applied Mathematical Modelling, 150, 116397. Article Preprint
Binois, M., Collier, N. & Ozik, J. (2025). A portfolio approach to massively parallel Bayesian optimization. Journal of Artificial Intelligence Research, 82, 137-167. Article Preprint
O’Gara, D., Binois, M., Garnett, R. & Hammond R. A. (2025). hetGPy: Heteroskedastic Gaussian process modeling in Python. Journal of Open Source Software, 10(106), 7518. Article
O’Gara, D., Kerr, C. C., Klein, D. J., Binois, M., Garnett, R. & Hammond, R. A. (2025). Improving policy-oriented agent-based modeling with history matching: A case study. Epidemics, 52, 100845. Article Preprint
Palazzolo, L., Binois, M., Berti, L. & Giraldi, L. (2025). Parametric shape optimization of flagellated microswimmers using Bayesian techniques. Physical Review Fluids, 10(3), 034101. Article Preprint
Mom, B., Thulliez, L., Dumonteil, É., Binois, M., Richet, Y., Schwindling J. & Drouart, A. (2022). Simulation and design of an IPHI-based neutron source, first steps toward SONATE. Journal of Neutron Research, 24(3-4), 337-345. Article
Pezzano, S., Duvigneau, R. & Binois, M. (2022). Geometrically consistent aerodynamic optimization using an isogeometric Discontinuous Galerkin method. Computers & Mathematics with Applications, 128(15), 368-381. Article Preprint
Würth, A., Binois, M., Goatin, P. & Göttlich, S. (2022). Data driven uncertainty quantification in macroscopic traffic flow models. Advances in Computational Mathematics, 48(6). Article Preprint
Binois, M., Wycoff, N. (2022). A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization. ACM Transactions on Evolutionary Learning and Optimization, 2(2), 1-26. Article Preprint
Wycoff, N., Binois, M. & Gramacy, R. (2022). Sensitivity Prewarping for Local Surrogate Modeling. Technometrics, 64(4), 535-547. Article Preprint
Elsawy, M., Binois, M., Duvigneau, R., Lanteri, S. & Genevet, P. (2021). Optimization of metasurfaces under geometrical uncertainty using statistical learning. Optics Express, 29, 29887-29898. Article
Elsawy, M., Gourdin, A., Binois, M., Duvigneau, R., Felbacq, D., Khadir, S., , Genevet, P. & Lanteri, S. (2021). Multiobjective statistical learning optimization for large-scale RGB metalens. ACS photonics, 8(8), 2498-2508. Article Preprint
Wycoff, N., Binois, M. & Wild, S. (2021). Sequential Learning of Active Subspaces. Journal of Computational and Graphical Statistics, 30(4), 1224-1237. Article Preprint
Ozik, J., Wozniak, J., Collier, N., Macal, C. & Binois, M. (2021). A Population Data-Driven Workflow for COVID-19 Modeling and Learning. International Journal of High Performance Computing Applications, 35(5), 483-499. Article Preprint.
Lyu, X., Binois, M. & Ludkovski, M. (2021). Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation. Statistics and Computing, 31(4). Article Preprint
Binois, M., Gramacy, R. B. (2021). hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R. Journal of Statistical Software, 98(13). Article Vignette
Binois, M., Picheny, V., Taillandier, P. & Habbal, A. (2020). The Kalai-Smorodinski solution for many-objective Bayesian optimization. Journal of Machine Learning Research 21 (150), 1-42. Article Preprint
Huang, J., Gramacy, R. B., Binois, M. & Librashi, M. (2020). On-site surrogates for large-scale calibration. Applied Stochastic Models in Business and Industry, 36, 283-304. Article Preprint
Binois, M., Ginsbourger, D. & Roustant, O. (2020). On the choice of the low-dimensional domain for global optimization via random embeddings. Journal of Global Optimization, 76, 69-90. Article Preprint
Chung, M., Binois, M., Gramacy, R.B., Moquin, D.J., Smith, A.P. & Smith, A.M. (2019). Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes. SIAM Journal on Scientific Computing 41(4), 2212-2238. Article Preprint
Picheny, V., Binois, M. & Habbal, A. (2019). A Bayesian optimization approach to find Nash equilibria. Journal of Global Optimization, 73(1), 171-192. Article Preprint
Binois, M., Huang, J., Gramacy, R. & Ludkovski, M. (2019). Replication or exploration? Sequential design for stochastic simulation experiments. Technometrics, 61(1), 7-23 . Article Preprint
Binois, M., Gramacy R. & Ludkovski, M. (2018). Practical heteroskedastic Gaussian process modeling for large simulation experiments. Journal of Computational and Graphical Statistics, 27(4), 808-821. Article Preprint
Binois, M. & Picheny, V. (2019). GPareto: An R Package for Gaussian-Process Based Multi-Objective Optimization and Analysis. Journal of Statistical Software, 89(8), 1-30. Article Vignette
Binois, M., Ginsbourger, D. & Roustant, O. (2015). Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. European Journal of Operational Research, 243(2), 386-394. Article Preprint
Binois, M., Rulliere, D. & Roustant, O. (2015). On the estimation of Pareto fronts from the point of view of copula theory. Information Sciences, 324, 270-285. Article Preprint
Désidéri, J.-A., Wintz, J., Binois, M., Bartoli, N., David, C. & Defoort S. (2026). Prioritized multi-objective optimization of an aircraft flight performance based on Nash games from preponderant Pareto-optimal points. In Challenges in Design Methods, Numerical Tools and Technologies for Sustainable Aviation, Transport and Industry: Commemorative publication dedicated to the 80th Jubilee of Prof. Jacques Periaux, pp. 11-25. Springer. Article Preprint
Binois, M., Branke, J., Fieldsend, J. & Purshouse, R. C. (2025). Decoupled design of experiments for expensive multi-objective problems. In Learning and Intelligent Optimization, pp. 37-50. Springer Nature Switzerland, Cham. Article Preprint
Fadikar, A., Binois, M., Collier, N., Stevens, A., Toh, K. & Ozik, J. (2023). Trajectory-oriented optimization of stochastic epidemiological models. In Winter Simulation Conference 2023. Preprint
Musayeva, K., Binois, M. (2023). Improved Multi-label Propagation for Small Data with Multi-objective Optimization. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 284 - 300. Article Preprint
Würth, A., Binois, M. & Goatin, P. (2023). Validation of calibration strategies for macroscopic traffic flow models on synthetic data. In 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 1-6. Article Preprint
Collier N., Wozniak J.M., Stevens, A., Babuji, Y., Binois, M., Fadikar, A., Würth, A., Chard, K. &Ozik, J. (2023). Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis. In 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 868-877). Article Preprint
Binois, M., Picheny, V. & Habbal, A. (2017). The Kalai-Smorodinski solution for many-objective Bayesian optimization. In BayesOpt workshop at NIPS 2017-31st Conference on Neural Information Processing Systems. Article Preprint
Crandell, I., Millican, A., Vasta, R., Smith, E., Alexander, N., Devenport, W., Gramacy, R. & Binois, M. (2017). Anomaly detection in large-scale wind tunnel tests using Gaussian processes. In 33rd AIAA Aerodynamic Measurement Technology and Ground Testing Conference. AIAA AVIATION Forum. Article
Binois, M., Ginsbourger, D. & Roustant, O. (2015). A warped kernel improving robustness in Bayesian optimization via random embeddings. In Learning and Intelligent Optimization (pp. 281-286). Springer International Publishing. Article Preprint
Binois, M., Habbal, A., Picheny, P. (2023). A game theoretic perspective on Bayesian multi-objective optimization. Many-Criteria Optimization and Decision Analysis : State-of-the-Art, Present Challenges, and Future Perspectives, pp. 299 - 316. Article Preprint
Palazzolo, L., Binois, M. & Giraldi, L. (2026). Optimal Control of Microswimmers for Trajectory Tracking Using Bayesian Optimization. Preprint
Binois, M., Larson, J. (2026). Adaptive replication strategies in trust-region-based Bayesian optimization of stochastic functions. Preprint
Fadikar, A., Stevens, A., Binois, M., Collier, N. & Ozik, J. (2025). Adaptive grid-based Thompson sampling for efficient trajectory discovery. Preprint
Palazzolo, L., Binois, M. & Giraldi, L. (2025). Non-locally controllable but trackable magnetic head flagellated swimmer. Preprint
Binois, M., Picheny, V. (2024). Combining additivity and active subspaces for high-dimensional Gaussian process modeling. Preprint
Binois, M., Fadikar, A. & Stevens, A. (2024). Gearing Gaussian process modeling and sequential design towards stochastic simulators. Preprint
Musayeva, K., Binois, M. (2024). Shared active subspace for multivariate vector-valued functions. Preprint
Berti, L., Binois, M., Alouges, F., Aussal, M., Prud'Homme, C. & Giraldi, L. (2021). Shapes enhancing the propulsion of multiflagellated helical microswimmers. Preprint
Habilitation thesis. Contributions to the scaling of Gaussian process metamodeling and Bayesian optimization, defended in April 2026. Link
PhD thesis. Uncertainty quantification on Pareto fronts and high-dimensional strategies in Bayesian optimization, with applications in multi-objective automotive design, defended in December 2015. Link