Education
Operations Research & Financial Engineering, PhD, Princeton University
Additional Campus Affiliations
Assistant Professor, Statistics
Assistant Professor, Industrial and Enterprise Systems Engineering
Affiliate, Carl R. Woese Institute for Genomic Biology
External Links
Recent Publications
Dayanıklı, G., & Laurière, M. (2025). A Machine Learning Method for Stackelberg Mean Field Games. Mathematics of Operations Research, 50(4), 3055-3093. https://doi.org/10.1287/moor.2023.0065
Cui, K., Dayanıklı, G., Laurière, M., Geist, M., Pietquin, O., & Koeppl, H. (2024). Learning Discrete-Time Major-Minor Mean Field Games. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Technical Tracks 14 (9 ed., pp. 9616-9625). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38, No. 9). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v38i9.28818
Dayanikli, G., & Lauriere, M. (2024). Multi-population Mean Field Games with Multiple Major Players: Application to Carbon Emission Regulations. In 2024 American Control Conference, ACC 2024 (pp. 5075-5081). (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC60939.2024.10644211
Dayanıklı, G., & Lauriere, M. (2024). A Machine Learning Method for Stackelberg Mean Field Games. Mathematics of Operations Research. https://pubsonline.informs.org/doi/abs/10.1287/moor.2023.0065
Dayanıklı, G., Laurière, M., & Zhang, J. (2024). Deep Learning for Population-Dependent Controls in Mean Field Control Problems with Common Noise. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2024-May, 2231-2233.