Prof. Theodore B. Trafalis

The University of Oklahoma, USA

Email: ttrafalis@ou.edu

Qualifications

1989  Ph.D., Purdue University, USA

1984  M.Sc., Purdue University, USA

1982  B.Sc., University of Athens, Greece

Publications (Selected)

1. Roberts-Licklider, K., & Trafalis, T. (2025, September). Optimizing Treatment Facility Locations in Oklahoma Using Haversine, Euclidean, Manhattan, and Chebyshev Distance Optimization. In Operations Research Forum (Vol. 6, No. 3, pp. 1-32). Springer International Publishing.
2. Obaid, H. B., Trafalis, T. B., Abushaega, M. M., Altherwi, A., & Hamzi, A. (2024). Optimizing Dynamic Evacuation Using Mixed-Integer Linear Programming. Mathematics, 13(1), 12.
3. Dong, X., Tan, T., Potter, M., Tsai, Y. C., Kumar, G., Saripalli, V. R., & Trafalis, T. (2024). To raise or not to raise: The autonomous learning rate question. Annals of Mathematics and Artificial Intelligence, 92(6), 1679-1698.
4. Jafarigol, E., Trafalis, T. B., Razzaghi, T., & Zamankhani, M. (2024). Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations. Dynamics of Disasters: From Natural Phenomena to Human Activity, 87-121.
5. Jafarigol, E., & Trafalis, T. B. (2024). A distributed approach to meteorological predictions: addressing data imbalance in precipitation prediction models through federated learning and GANs. Computational Management Science, 21(1), 22.
6. Jafarigol, E., & Trafalis, T. (2023). The paradox of noise: An empirical study of noise-infusion mechanisms to improve generalization, stability, and privacy in federated learning. arXiv preprint arXiv:2311.05790.
7. Jafarigol, E., Keely, W., Hortag, T., Welborn, T., Hekmatpour, P., & Trafalis, T. B. (2023). Religious affiliation in the twenty-first century: A machine learning perspective on the world value survey. Society, 60(5), 733-749.
8. Almaraj, I. I., & Trafalis, T. B. (2022). A robust optimization approach in a multi-objective closed-loop supply chain model under imperfect quality production. Annals of Operations Research, 319(2), 1479-1505.
9. Trafalis, T. B., & Couellan, N. P. (2021, September). Neural network training via a primal-dual interior point method for linear programming. In World Congress on Neural Networks (pp. II-798). Routledge.
10. Jafarigol, E., & Trafalis, T. (2020). Imbalanced learning with parametric linear programming support vector machine for weather data application. SN Computer Science, 1(6), 360.
11. Ahsan, M. M., E. Alam, T., Trafalis, T., & Huebner, P. (2020). Deep MLP-CNN model using mixed-data to distinguish between COVID-19 and Non-COVID-19 patients. Symmetry, 12(9), 1526.
12. Almaraj, I. I., & Trafalis, T. B. (2020). Affinely adjustable robust optimization under dynamic uncertainty set for a novel robust closed-loop supply chain. Computers & Industrial Engineering, 145, 106521.
13. Bin Obaid, H. S., & Trafalis, T. B. (2020). An approximation to max min fairness in multi commodity networks. Computational Management Science, 17(1), 65-77.
14. Almaraj, I. I., & Trafalis, T. B. (2019). An integrated multi-echelon robust closed-loop supply chain under imperfect quality production. International Journal of Production Economics, 218, 212-227.
15. Bin-Obaid, H. S., & Trafalis, T. B. (2018, May). Fairness in resource allocation: Foundation and applications. In International Conference on Network Analysis (pp. 3-18). Cham: Springer International Publishing.

Profile Details

https://www.ou.edu/coe/ise/people/faculty/theodore-b-trafalis

https://scholar.google.com/citations?user=llfzuLQAAAAJ&hl=en

https://www.researchgate.net/profile/Theodore-Trafalis