Prof. Theodore B. Trafalis

The University of Oklahoma, USA

Email: [email protected]

Qualifications

1989 Ph.D., Industrial Engineering, Purdue University, USA
1984 M.Sc., Mathematics, Purdue University, USA
1982 B.Sc., Mathematics, University of Athens, Greece

Publications (Selected)

1. Rowland, O. E., Trafalis, T., Seyidova, N., et al. (2026). Weather-electricity demand relationships: A comparative analysis of linear regression and machine-learning approaches in Texas. Sustainable Energy, Grids and Networks, 47, Article 102483.
2. Sun, W., & Trafalis, T. B. (2025). Risk-aware controller implementation for risk-sensitive mean field games through a game-theoretic differential dynamic programming approach. International Journal of Control, 98(12), 2967–2975.
3. Sun, W., & Trafalis, T. B. (2025). H∞-Optimal control via game-theoretic differential dynamic programming and Gaussian processes. Journal of Optimization Theory and Applications, 204(3), Article 40.
4. Obaid, H. B., Trafalis, T. B., Abushaega, M. M., et al. (2025). Optimizing dynamic evacuation using mixed-integer linear programming. Mathematics, 13(1), Article 12.
5. Dong, X. M., Tan, T., Potter, M., et al. (2024). To raise or not to raise: The autonomous learning rate question. Annals of Mathematics and Artificial Intelligence, 92(6), 1679–1698.
6. Ahsan, M. M., Alam, T. E., Trafalis, T., et al. (2020). Deep MLP-CNN model using mixed-data to distinguish between COVID-19 and non-COVID-19 patients. Symmetry, 12(9), Article 1526.
7. Aleskerov, F., Demin, S., Richman, M. B., et al. (2020). Constructing an efficient machine learning model for tornado prediction. International Journal of Information Technology & Decision Making, 19(5), 1177–1187.
8. Malyscheff, A. M., & Trafalis, T. B. (2020). Kernel classification using a linear programming approach. Annals of Mathematics and Artificial Intelligence, 88(1–3), 39–51.
9. Maalouf, M., Homouz, D., & Trafalis, T. B. (2018). Logistic regression in large rare events and imbalanced data: A performance comparison of prior correction and weighting methods. Computational Intelligence, 34(1), 161–174.
10. Gilbert, R. C., Trafalis, T. B., Richman, M. B., et al. (2017). A data-driven kernel method assimilation technique for geophysical modelling. Optimization Methods & Software, 32(2), 237–249.
11. Tokgöz, E., & Trafalis, T. B. (2017). 2-Facility manifold location routing problem. Optimization Letters, 11(2), 389–405.
12. Ince, H., & Trafalis, T. B. (2017). A hybrid forecasting model for stock market prediction. Economic Computation and Economic Cybernetics Studies and Research, 51(3), 263–280.
13. Richman, M. B., Leslie, L. M., Trafalis, T. B., et al. (2015). Data selection using support vector regression. Advances in Atmospheric Sciences, 32(3), 277–286.
14. MacKenzie, C. A., Trafalis, T. B., & Barker, K. (2014). A Bayesian Beta kernel model for binary classification and online learning problems. Statistical Analysis and Data Mining: The ASA Data Science Journal, 7(6), 434–449.
15. Chen, V. C. P., Kim, S. B., & Trafalis, T. (2014). Preface: Special volume on data mining and informatics. Annals of Operations Research, 216(1), 1–2.
16. Couellan, N. P., & Trafalis, T. B. (2013). An incremental primal-dual method for nonlinear programming with special structure. Optimization Letters, 7(1), 51–62.
17. Couellan, N. P., & Trafalis, T. B. (2013). On-line SVM learning via an incremental primal-dual technique. Optimization Methods & Software, 28(2), 256–275.
18. Maalouf, M., & Trafalis, T. B. (2011). Robust weighted kernel logistic regression in imbalanced and rare events data. Computational Statistics & Data Analysis, 55(1), 168–183.
19. Oladunni, O. O., & Trafalis, T. B. (2011). Single-phase fluid flow classification via learning models. International Journal of General Systems, 40(5), 561–576.
20. Adrianto, I., & Trafalis, T. B. (2010). The p-Centre machine for regression analysis. Optimization Methods & Software, 25(2), 171–183.
21. Trafalis, T. B., & Alwazzi, S. A. (2010). Support vector machine classification with noisy data: A second order cone programming approach. International Journal of General Systems, 39(7), 757–781.
22. Oladunni, O. O., & Trafalis, T. B. (2009). A regularized pairwise multi-classification knowledge-based machine and applications. European Journal of Operational Research, 195(3), 924–941.

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

WOS ResearcherID: DYT-8420-2022