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Gökçe Dayanıklı

Assistant Professor

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

Recent Publications

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

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.

Dehghanimohammadabadai, M., & Dayanikli, G. (2023). Enhancing Pandemic Preparedness Using Mean Field and Simulation Modeling. In 2023 Winter Simulation Conference, WSC 2023 (pp. 970-981). (Proceedings - Winter Simulation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC60868.2023.10408053

Aurell, A., Carmona, R., Dayanıklı, G., & Laurière, M. (2022). Finite State Graphon Games with Applications to Epidemics. Dynamic Games and Applications, 12(1), 49-81. https://doi.org/10.1007/s13235-021-00410-2

Aurell, A., Carmona, R., Dayanikli, G., & Laurière, M. (2022). Optimal Incentives to Mitigate Epidemics: A Stackelberg Mean Field Game Approach: A STACKELBERG MEAN FIELD GAME APPROACH. SIAM Journal on Control and Optimization, 60(2), S294-S322. https://doi.org/10.1137/20M1377862

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