Dr. J. (Jannis) Kurtz
Assistant Professor in Mathematical Methods and Applications in Data Science
Faculty of Economics and Business
Section Business Analytics
Visiting address
- Plantage Muidergracht 12
Postal address
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Postbus 15953
1001 NL Amsterdam
Contact details
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Publications
2026
- Kurtz, J. (in press). Bounding the Optimal Number of Policies for Robust K-Adaptability. Mathematical programming.
- Kurtz, J., Birbil, Ş. İ., & den Hertog, D. (2026). Counterfactual explanations for linear optimization. European Journal of Operational Research, 329(1), 24-41. Advance online publication. https://doi.org/10.1016/j.ejor.2025.06.016
2025
Goerigk, M., & Kurtz, J. (2025). Data-driven prediction of relevant scenarios for robust combinatorial optimization. Computers & Operations Research, 174, Article 106886. https://doi.org/10.1016/j.cor.2024.106886 [details]- Goerigk, M., Kurtz, J., Schmidt, M., & Thürauf, J. (2025). Connections between Robust and Bilevel Optimization. Open Journal of Mathematical Optimization, 6, Article 2. https://doi.org/10.5802/ojmo.38
- Otto, D., Kurtz, J., & Birbil, S. I. (2025). Coherent Local Explanations for Mathematical Optimization. Manuscript submitted for publication.
- Wasserkrug, S., Boussioux, L., den Hertog, D., Mirzazadeh, F., Birbil, S. I., Kurtz, J., & Maragno, D. (2025). Enhancing decision making through the integration of large language models and operations research optimization. In Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (27 ed., Vol. 39, pp. 28643-28650). (Proceedings of the AAAI Conference on Artificial Intelligence). https://doi.org/10.1609/aaai.v39i27.35090
2024
- Dumouchelle, J., Julien, E., Kurtz, J., & Khalil, E. (2024). Neur2RO: Neural Two-Stage Robust Optimization. In The Twelfth International Conference on Learning Representations International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=T5Xb0iGCCv
- Dumouchelle, J., Julien, E., Kurtz, J., & Khalil, E. B. (2024). Neur2BiLO: Neural Bilevel Optimization. In 38th Conference on Neural Information Processing Systems (NeurIPS 2024): Held 10-15 December 2024, Vancouver, Canada (pp. 86688-86719). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-2752 [details]
- Kurtz, J. (2024). Approximation Guarantees for Min-Max-Min Robust Optimization and k-Adaptability Under Objective Uncertainty. SIAM Journal on Optimization, 34(2), 2121-2149. https://doi.org/10.1137/23M1595084 [details]
- Maragno, D., Kurtz, J., Röber, T. E., Goedhart, R., Birbil, Ş. İ., & den Hertog, D. (2024). Finding regions of counterfactual explanations via robust optimization. INFORMS Journal on Computing, 36(5), 1316–1334. https://doi.org/10.1287/ijoc.2023.0153 [details]
2023
- Goerigk, M., & Kurtz, J. (2023). Data-Driven Robust Optimization using Unsupervised Deep Learning. Computers & Operations Research, 151, Article 106087. https://doi.org/10.1016/j.cor.2022.106087 [details]
2021
- Bah, B., Kurtz, J., & Schaudt, O. (2021). Discrete optimization methods for group model selection in compressed sensing. Mathematical programming, 190(1-2), 171-220. https://doi.org/10.1007/s10107-020-01529-7
2020
- Eufinger, L., Kurtz, J., Buchheim, C., & Clausen, U. (2020). A Robust Approach to the Capacitated Vehicle Routing Problem with Uncertain Costs. INFORMS Journal on Optimization, 2(2), 79-95. https://doi.org/10.1287/ijoo.2019.0021
- Goerigk, M., Kurtz, J., & Poss, M. (2020). Min–max–min robustness for combinatorial problems with discrete budgeted uncertainty. Discrete Applied Mathematics, 285, 707-725. https://doi.org/10.1016/j.dam.2020.07.011
- Kaemmerling, N., & Kurtz, J. (2020). Oracle-based algorithms for binary two-stage robust optimization. Computational Optimization and Applications, 77(2), 539-569. https://doi.org/10.1007/s10589-020-00207-w
2019
- Chassein, A., Goerigk, M., Kurtz, J., & Poss, M. (2019). Faster algorithms for min-max-min robustness for combinatorial problems with budgeted uncertainty. European Journal of Operational Research, 279(2), 308-319. https://doi.org/10.1016/j.ejor.2019.05.045
2018
- Buchheim, C., & Kurtz, J. (2018). Complexity of min–max–min robustness for combinatorial optimization under discrete uncertainty. Discrete Optimization, 28, 1-15. https://doi.org/10.1016/j.disopt.2017.08.006
- Buchheim, C., & Kurtz, J. (2018). Robust combinatorial optimization under convex and discrete cost uncertainty. EURO Journal on Computational Optimization, 6(3), 211-238. https://doi.org/10.1007/s13675-018-0103-0
- Kurtz, J. (2018). Robust combinatorial optimization under budgeted-ellipsoidal uncertainty. EURO Journal on Computational Optimization, 6(4), 315-337. https://doi.org/10.1007/s13675-018-0097-7
2017
- Buchheim, C., & Kurtz, J. (2017). Min–max–min robust combinatorial optimization. Mathematical programming, 163(1-2), 1-23. https://doi.org/10.1007/s10107-016-1053-z
2016
- Buchheim, C., & Kurtz, J. (2016). Min-max-min robustness: A new approach to combinatorial optimization under uncertainty based on multiple solutions. Electronic Notes in Discrete Mathematics, 52, 45-52. https://doi.org/10.1016/j.endm.2016.03.007
2014
- Kawohl, B., Kroemer, S., & Kurtz, J. (2014). Radial Eigenfunctions for the Game-Theoretic p-Laplacian on a Ball. Differential and Integral Equations, 27(7-8), 659-670.
2025
- Besançon, M., & Kurtz, J. (2025). A Frank-Wolfe Algorithm for Oracle-based Robust Optimization. Manuscript in preparation.
- Dumouchelle, J., Julien, E., Kurtz, J., & Khalil, E. (2025). Deep Learning for Two-Stage Robust Integer Optimization. Manuscript submitted for publication.
- Engelhardt, F., Kurtz, J., Birbil, S. I., & Ralphs, T. (2025). Counterfactual Explanations for Integer Optimization Problems. Manuscript submitted for publication.
- Qiu, Z., Adjari, A., Bóbic, M., Bortfeld, T., den Hertog, D., Kurtz, J., & Lee, H. (2025). A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning. ArXiv. https://doi.org/10.48550/arXiv.2508.07368
2022
- Kurtz, J. (2022). Ensemble Methods for Robust Support Vector Machines using Integer Programming. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2203.01606 [details]
2021
- Kurtz, J. (2021). New complexity results and algorithms for min-max-min robust combinatorial optimization. ArXiv. https://doi.org/10.48550/arXiv.2106.03107
- Kurtz, J., & Bah, B. (2021). Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks. ArXiv. https://doi.org/10.48550/arXiv.2110.11382
2020
- Bah, B., & Kurtz, J. (2020). An integer programming approach to deep neural networks with binary activation functions. (v3 ed.) ArXiv. https://doi.org/10.48550/arXiv.2007.03326
Talk / presentation
- Kurtz, J. (speaker) (20-9-2024). Deep Learning in Robust Optimization, Computational Optimization at Work. https://www.youtube.com/watch?v=nX7RLEDGDEY
Others
- Kurtz, J. (other) & Marandi, A. (other) (1-1-2023). Co-Organizer of the Robust Optimization Webinar (ROW) (other).
2024
- Kurtz, J., Birbil, S. I., & den Hertog, D. (2024). Counterfactual explanations for linear optimization. ArXiv. https://doi.org/https://arxiv.org/abs/2405.15431, https://doi.org/10.48550/arXiv.2405.15431
- Wasserkrug, S., Boussioux, L., den Hertog, D., Mirzazadeh, F., Birbil, S. I., Kurtz, J., & Maragno, D. (2024). From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto. ArXiv. https://arxiv.org/abs/2402.16269
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Ancillary activities
No ancillary activities
Goerigk, M., & Kurtz, J. (2025). Data-driven prediction of relevant scenarios for robust combinatorial optimization. Computers & Operations Research, 174, Article 106886.