Ph.D., Industrial Engineering, University of Central Florida
M.S., Industrial and Systems Engineering, Sharif University of Technology, Iran
B.S., Applied Mathematics, University of Tehran, Iran

Talayeh Razzaghi is an assistant professor in the School of Industrial and Systems Engineering at the University of Oklahoma.  She received her PhD in industrial engineering from the University of Central Florida, and her Master’s degree in industrial engineering from Sharif University of Technology in Iran. Upon graduation, Dr. Razzaghi worked served as a postdoctoral research associate at Clemson University’s School of Computing. While at Clemson University, she also jointly served as an embedded scholar at the Greenville Health System. Prior to joining OU, Dr. Razzaghi worked as an assistant professor at the New Mexico State University’s Industrial Engineering department.  Her research program at OU focuses on the development and use of data-driven analytical models to guide decision making for real-world problems, particularly energy analytics, smart manufacturing, and healthcare informatics. In her research, she primarily employs the theory of machine learning and data mining for the settings with the presence of imperfect, noisy, and possibly massive datasets.

Machine learning, big data analytics, predictive modeling, mathematical modeling, energy systems, healthcare and bioinformatics.

• Sadrfaridpour, E., Razzaghi, T. , Safro, I. (2019). “Engineering Fast Multilevel Support Vector Machines.” Machine Learning , 1–39.
• Razzaghi, T., Safro, I., Ewing, J., Sadrfaridpour, E., & Scott, J. D. (2017). Predictive models for bariatric surgery risks with imbalanced medical datasets. Annals of Operations Research, 1-18.
• Panagopoulos, O. P., Razzaghi, T. , Xanthopoulos, P., and ¸Seref, O. (2018). “Relaxed Support Vector Regression.” Annals of Operations Research , 1-20.
• Razzaghi, T., Xanthopoulos, P., and ¸Seref, O. (2017). “Constraint Relaxation, Cost-sensitive Learning and Bagging for Imbalanced Classification Problems with Outliers.” Optimization Letters, 11(5), 915-928.

List of all publications: Google Scholar