Maragno, D., Wiberg, H., Bertsimas, D., Birbil, S., den Hertog, D. and Fajemisin, A. (2025). Mixed-Integer Optimization with Constraint Learning Operations Research, :.
Ilker Birbil
University
University of Amsterdam
E-mail
Research field
Operations Analytics
Website
Biography
Ilker Birbil is a professor of AI & Optimization Techniques for Business & Society in University of Amsterdam (UvA), where I am a faculty member at the Business Analytics section of the Amsterdam Business School (ABS). Ilker's research interests center around optimization methods in data science and decision making. Lately, Ilker is interested in interpretable machine learning and data privacy in operations research.
Research keywords: Mathematical methods and applications in data science and decision making, Explainable artificial intelligence and Explainable optimization
Publications
Maragno, D., Wiberg, H., Bertsimas, D., Birbil, S., den Hertog, D. and Fajemisin, A. (2025). Mixed-Integer Optimization with Constraint Learning Operations Research, :.
Maragno, D., Kurtz, J., Röber, T., Goedhart, R., Birbil, Ş. and den Hertog, D. (2024). Finding regions of counterfactual explanations via robust optimization INFORMS Journal on Computing, 36(5):1316–1334.
Vogels, L., Mohammadi, A., Schoonhoven, M. and Birbil, S. (2024). Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison Journal of the American Statistical Association, 119(548):3164--3182.
U, K., Birbil, S., Aydin, N. and Mullaoğlu, G. (2023). Masking Primal and Dual Models for Data Privacy in Network Revenue Management European Journal of Operational Research, 308(2):818--831.
Karaca, U., Birbil, S.Ilker, Yildirim, S., Aydin, N. and Mullaoglu, G. (2022). Differential Privacy in Multi-Party Resource Sharing Operations Research, :.
Julien, E., Postek, K. and Birbil, S. (2022). Machine Learning for K-adaptability in Two-Stage Robust Optimization INFORMS Journal on Computing, :.
Lumadjeng, AdiaC., Röber, T., Akyuz, H. and Birbil, S. (2022). Rule Generation for Classification: Scalability, Interpretability, and Fairness Production and Operations Management, :.