Prof. dr. S.I. (Ilker) Birbil
Professor of AI & Optimization Techniques for Business & Society
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
Social media
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Publications
- refereed (15)
- academic (3)
- recognitions (16)
- supervised theses (1)
- working papers / preprints (2)
- datasets (1)
2025
- Maragno, D., Wiberg, H., Bertsimas, D., Birbil, S. I., den Hertog, D., & Fajemisin, A. O. (2025). Mixed-Integer Optimization with Constraint Learning. Operations Research, 73(2), 1011-1028. https://doi.org/10.1287/opre.2021.0707
2024
Birbil, İ., Martin, Ö., Onay, G., & Öztoprak, F. (2024). Bolstering stochastic gradient descent with model building. TOP, 32(3), 517-536. https://doi.org/10.1007/s11750-024-00673-z [details]- Maragno, D., Buti, G., Birbil, S. I., Liao, Z., Bortfeld, T., den Hertog, D., & Ajdari, A. (2024). Embedding machine learning based toxicity models within radiotherapy treatment plan optimization. Physics in Medicine and Biology, 69(7), Article 075003. https://doi.org/10.1088/1361-6560/ad2d7e [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]
- Vogels, L., Mohammadi, R., Schoonhoven, M., & Birbil, S. I. (2024). Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison. Journal of the American Statistical Association, 119(548), 3164-3182. https://doi.org/10.1080/01621459.2024.2395504 [details]
- von Stackelberg, P., Goedhart, R., Birbil, S. I., & Does, R. J. M. M. (2024). Comparison of threshold tuning methods for predictive monitoring. Quality and Reliability Engineering International, 40(1), 499-512. https://doi.org/10.1002/qre.3436 [details]
2023
- Cina, G., Röber, T. E., Goedhart, R., & Birbil, S. I. (2023). Semantic match: Debugging feature attribution methods in XAI for healthcare. Proceedings of Machine Learning Research, 209, 182-191. https://proceedings.mlr.press/v209/cina23a.html [details]
- Karaca, U., Birbil, S. I., Aydin, N., & Mullaoğlu, G. (2023). Masking Primal and Dual Models for Data Privacy in Network Revenue Management. European Journal of Operational Research, 308(2), 818-831. https://doi.org/10.1016/j.ejor.2022.11.025 [details]
2022
- Birbil, S. I., Yildirim, S., Gökalp, K., & Akyuz, M. H. (2022). Learning with Subset Stacking. Manuscript submitted for publication.
- Dekker, R., Koot, P., Birbil, S. I., & van Embden Andres, M. (2022). Co-designing Algorithms for Governance: Ensuring Responsible and Accountable Algorithmic Management of Refugee Camp Supplies. Big Data & Society, 9(1), 1-15. https://doi.org/10.1177/20539517221087855 [details]
- Julien, E., Postek, K., & Birbil, S. I. (2022). Machine Learning for K-adaptability in Two-Stage Robust Optimization. Manuscript submitted for publication.
- Karaca, U., Birbil, S. I., Yildirim, S., Aydin, N., & Mullaoglu, G. (2022). Differential Privacy in Multi-Party Resource Sharing. Manuscript submitted for publication.
- Kuru, N., Birbil, S. I., Gurbuzbalaban, M., & Yildirim, S. (2022). Differentially Private Accelerated Optimization Algorithms. SIAM Journal on Optimization, 32(2), 795-821. https://doi.org/10.1137/20M1355847
- Lumadjeng, A. C., Röber, T. E., Akyuz, H., & Birbil, S. I. (2022). Rule Generation for Classification: Scalability, Interpretability, and Fairness. Manuscript submitted for publication.
- Maragno, D., Röber, T. E., & Birbil, S. İ. (2022). Counterfactual Explanations Using Optimization With Constraint Learning. In OPT2022: Optimization for Machine Learning. Accepted papers OPT-ML. https://doi.org/10.48550/arXiv.2209.10997 [details]
2022
- Cina, G., Röber, T., Goedhart, R., & Birbil, I. (2022). Why we do need Explainable AI for Healthcare. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2206.15363 [details]
2021
- Birbil, S. I. (Author), & Martin, O. (Author). (2021). Stochastic Model Building: SMB. Software, GitHub.
- Birbil, S. I. (Author). (2021). Learning with Subset Stacking: LESS. Software, GitHub.
Membership / relevant position
- Birbil, S. . (2021). Optimization and Machine Learning Group (LNMB). https://www.lnmb.nl/pages/dutchorgroups/proj10b.htm
- Birbil, S. . (2021). Optimisation for and with Machine Learning (OPTIMAL). https://optimal.uva.nl
Talk / presentation
- Birbil, S. . (keynote speaker) (28-4-2022). IPD2022: Innovative Product Development International Conference, Kasetsart University. https://easychair.org/cfp/IPD2022
- Birbil, S. . (invited speaker) (12-4-2022). Data Science in Finance Conference - 2022, ABN AMRO. https://dsfc.nl/schedule-2022/
- Birbil, S. . (keynote speaker) (7-3-2022). 12th Annual International Conference on Industrial Engineering and Operations Management, Kadir Has University, Istanbul, Turkey.. https://ieomsociety.org/istanbul2022/
- Birbil, S. . (speaker) (29-9-2021). Optimization and Machine Learning: Overview and Current Research, Machine Learning for Optimization. https://video.uva.nl/media/Lecture+3.1/0_3cm8tcfl
- Birbil, S. . (speaker) (24-6-2021). Yapay Öğrenmede Yorumlanabilirlik, Sabanci University - Academy of Science: Machine Learning Summer School 2021. https://www.youtube.com/watch?v=83HHZ5INDx0
- Birbil, S. . (speaker) (11-1-2021). Rule Generation for Learning and Interpretation, Machine Learning NeEDS Mathematical Optimization. https://www.youtube.com/watch?v=ekcQEYd9C88
Others
- Birbil, S. . (organiser), Romero Morales, D. (organiser), Rudin, C. (organiser), Fokkema, D. (organiser) & Akyuz, H. (participant) (17-10-2022 - 21-10-2022). Making Sense of Interpretable Machine Learning, Leiden (organising a conference, workshop, ...).
- Birbil, S. . (examiner) (7-9-2022). Molding the Symbiosis Between Human and Machine (examination).
- Birbil, S. . (examiner) (6-4-2022). Numerical Optimal Control of Open Channel Networks (examination).
- Birbil, S. . (organiser) & Martin, O. (organiser) (26-3-2022 - 27-3-2022). Stochastic Optimization for Large-Scale Machine Learning, Istanbul (organising a conference, workshop, ...).
- Birbil, S. . (chair) (24-3-2022). EURO WISDOM Forum YoungWomen4OR Talks: Data Science and Optimization Webinar (participating in a conference, workshop, ...).
- Fajemisin, A. (participant), Maragno, D. (participant), den Hertog, D. (participant) & Birbil, S. . (participant) (22-2-2022 - 1-3-2022). 36th AAAI conference on Artificial Intelligence, Vancouver. Delivering two papers at the AI for Decision Optimization (AI4DO) workshop (participating in a conference, workshop, ...).
- Birbil, S. . (organiser), Postek, K. (organiser) & den Hertog, D. (organiser) (29-9-2021). Machine Learning for Optimization (organising a conference, workshop, ...). https://optimal.uva.nl/content/events/events/2021/09/machine-learning-for-optimization-workshop.html
- Birbil, S. . (organiser) (20-9-2021). Mathematics for Machine Learning, Izmir (organising a conference, workshop, ...). https://nesinkoyleri.org/events/2021-yapay-ogrenme-icin-matematik/
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
- 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
2024
- Vogels, L., Mohammadi, R., Schoonhoven, M. & Birbil, Ş. . (2024). Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison. Taylor & Francis. https://doi.org/10.6084/m9.figshare.26880600.v1
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Ancillary activities
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D4C - Data Science Consultants
Academic adviser for technical details of ongoing projects. -
D4C - Data Science Consultants
Adviser -
Centrum Wiskunde & Informatica
Research associate for at most one day a week.
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D4C - Data Science Consultants
Birbil, İ., Martin, Ö., Onay, G., & Öztoprak, F. (2024). Bolstering stochastic gradient descent with model building. TOP, 32(3), 517-536.