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Biography


Prof. Theodore B. Trafalis

The University of Oklahoma, USA


Email: ttrafalis@ou.edu


Qualifications

1989  Ph.D, Purdue University, USA

1987  M.S.I.E., Purdue University, USA

1984  M.S., Mathematics, Purdue University, USA

1982  B.S., Mathematics, University of Athens, Greece


Publications (selected)


  1. Trafalis, T.B., O.O. Oladunni and M. Richman, “Linear Classification Tikhonov Regularization Knowledge-based Support Vector Machine for Tornado Forecasting,” submitted to Computational Management Science, in press, 2010.
  2. Adrianto, I. and T.B. Trafalis, “The p-center Machine for Regression analysis”, Optimization Methods and Software, accepted, 2009.
  3. Gilbert R. C., T.B. Trafalis, M. B. Richman and S. Lakshmivarahan, “Real-time Prediction Using Kernel Methods and Data Assimilation”, series in Intelligent Engineering Systems Through Artificial Neural Networks, Computational Intelligence in Architecturing Complex Engineering Systems, pp. 35-42.ASME Press, New York, NY, USA, 2009.
  4. Oladunni, O.O. and T.B. Trafalis, “A regularized Pairwise Multi-classification Knowledge-based Machine and Applications”, European Journal of Operational Research, 195(3):924-941, 2009.
  5. Trafalis, T.B. and S. Kasap, “Neural Networks for Combinatorial Optimization”, in Encyclopedia of Optimization, (C.A. Floudas and P.M. Pardalos, editors), second edition, pp. 2547-2555, Springer, New York, NY, USA, 2009.
  6. Gilbert, R.C., S. Raman, T.B. Trafalis, S.M. Obeidat, and J.A. Aguirre-Cruz, “Mathematical Foundations for Form Inspection and Adaptive Sampling”, Journal of Manufacturing Science and Engineering, 2009.  Accepted, in press.
  7. Adrianto, I, T.B. Trafalis and M.B. Richman, “Active Learning with Kernel Machines for Tornado Detection”, in Intelligent Engineering Systems Through Artificial Neural Networks, 8:131-137, New York, NY, USA, 2008, ASME Press.
  8. Maalouf, M. and T.B. Trafalis, “Kernel Logistic Regression using Truncated Newton Method”, in Intelligent Engineering Systems Through Artificial Neural Networks, (C.H. Dagli, D.L. Enke, K.M. Bryden, Y. Ceylan and M. Gen, editors), 18:455-462, New York, NY, USA, ASME Press, 2008.
  9. Trafalis, T.B. and R.C. Gilbert, “Nonlinear Programming”, in Operations Research and Management Science Handbook, (A.R. Ravindran, editor), CRC pp. 2-1 to 2-22, 2008.
  10. Mansouri, H., M.B. Richman, T.B. Trafalis, and L.M. Leslie, “ Pipeline Support Vector Regression Method to Thin large Ocean Surface Wind Data On-line”, in Intelligent Engineering Systems Through Artificial Neural Networks, (C.H. Dagli, D.L. Enke, K.M. Bryden, H. Ceylan, and M. Gen, editors), 18:203-210, New York, NY,USA, 2008, ASME Press.
  11. Richman, M.B., T.B. Trafalis and I. Adrianto, “Missing Data Imputation through Machine Learning Algorithms”, in Artificial Intelligence Methods in the Environmental Sciences, (S.E. Haupt, A. Pasini and C. Marzban, editors), pp 153-169, Springer, London, UK, 2008.
  12. Oladunni, O.O. and T.B. Trafalis, “A Nonlinear Multi-classification Knowledge-based Kernel Machine”, Computational Management Science, 2008.  Available online.
  13. Kundakcioglu, O.E., M. Sanguineti and T.B. Trafalis, Guest editorial, Computational Management Science, 2008.  Available online.
  14. Maalouf, M., N. Khoury and T.B. Trafalis, “Support Vector Regression to Predict Asphalt Mix Performance”, International Journal for Numerical and Analytical Methods in Geomechanics, 32(16):1989-1996, 2008.
  15. Balakrishna, P., S. Raman, T.B. Trafalis and B. Santosa, “Support Vector Regression for Determining the Minimum Zone Sphericity”, International Journal of Advanced Manufacturing Technology, 35(9-10):916-923, 2008.
  16. Alenezi, A., S.A. Moses, and T.B. Trafalis, “Real-time Prediction of Order Flowtimes using Support Vector Regression, Computers and Operations Research, 35(11):3489-3503, 2008.
  17. Papadakis, P., I. Pratikakis, T.B. Trafalis, T. Theoharis and S. Perantonis, “Relevance Feedback in Content-based 3D Object Retrieval – A Comparative Study,” Computer Aided Design and Applications (CAD&A), 5(5):753-763, 2008.
  18. Oladunni, O.O. and T.B. Trafalis, “A Regularized Pairwise Multi-classification Knowledge-based Machine and Applications,” European Journal of Operational Research, pp. 643-689, 2008.
  19. Adrianto, I., Trafalis, T. B., & Lakshmanan, V., “Support vector machines for spatiotemporal tornado prediction”, International Journal of General Systems, Volume 38, Issue 7, Pages 759 – 776, 2009.
  20. Trafalis, T. B. and R. C. Gilbert. “Nonlinear Programming,” in Operations Research and Management Science Handbook, A. R. Ravindran, editor, CRC pp. 2-1 to 2-22, 2008.
  21. Ince, H. and T.B. Trafalis, “Short term Forecasting with Support Vector Machines and Application to Stock Price prediction”, International Journal of General Systems, 37(6):677-687, 2008.
  22. Trafalis, T.B. and R. C. Gilbert, “Robust Support Vector Machines for Classification and Computational Issues”, Optimization Methods and Software, 22(1):187-198, 2007.
  23. Trafalis, T.B. and O.O. Oladunni, “Support Vector Machines and Applications,” invited book chapter in Data Mining of Enterprise Data, Springer, T. Warren Liao and E. Triantaphyllou, eds., World Scientific, 14:643-690, 2007.
  24. Oladunni, O.O. and T.B. Trafalis, “Regularized Knowledge-based Kernel Machine,” ICCS 2007, Lecture notes in Computer Science, (Y. Shi et al., eds.), Part I, LNCS 4487, Springer-Verlag Berlin Heidelberg, pp.176-183, 2007.
  25. Santosa, B. and T.B. Trafalis, “Robust Multiclass Kernel-based Classifiers”, Computational Optimization, 38(2):261-280. 2007.
  26. Oladunni, O.O. and T.B. Trafalis, “Regularization based Classification Models,” Proceedings of the International Joint Conference on Neural (IJCNN’07), IEEE Press, pp. 25 – 30, 2007, on CDROM.
  27. Ince, H. and T.B. Trafalis, “Kernel Principal Component Analysis and Support Vector Machines for Stock Price Prediction”, special issue of the IIE Transactions on Quality and Reliability, 39(6):629-637, 2007.
  28. T.B. Trafalis and S. Alwazzi, “Support vector regression with noisy data: A second order cone programming approach”, International Journal of General Systems, 36(2):237-250, 2007.
  29. Santosa, B., T. Conway and T.B. Trafalis, “A Hybrid Knowledge Base-Clustering Multi-Class SVM for Genes Expression Analysis”, Data Mining in Biomedicine, P.M. Pardalos, V. Boginski and A. Vazacopoulos (eds.) Springer, pp. 261-274 , February 2007.
  30. Alenezi, A., S. A. Moses and T. B. Trafalis, “Real-Time Prediction of Order Flowtimes Using Support Vector Regression”, special issue of Computers & Operations Research on real-time supply chain management, 35(11):3489-3503, available online 12 February 2007.


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