|Office:||Ed and Harold Foreman Engineering Complex, Rm 288|
|Phone:||Office: (575) 646-4631|
- Doctor Of Philosophy, Industrial Engineering, University of Central Florida, 2014 (Under the supervision of Dr. Petros Xanthopoulos).
- Master of Science, Industrial Engineering, Sharif University of Technology, 2007
- Bachelor of Science, Applied Mathematics, University of Tehran, 2005
Dr. Talayeh Razzaghi has been working on machine learning and data mining from both theoretical and practical standpoints. Her main research interests are predictive models for real-world problem particularly healthcare applications using machine learning and data mining techniques. Examples of her work are classification models for data sets with imbalanced-ness and outliers, which are prevalent in healthcare applications (e.g., disease diagnosis) as well as several other business applications. She has also worked on data-science problems involving text categorization and pattern recognition for massive data sets arising in real-world applications. The following is a non-exhaustive list of her research interests
- Machine learning and data mining
- Predictive models for healthcare applications
- Massive datasets
- Pattern recognition
- Optimization problems in machine learning
Please refer to my Google Scholar page.
- Razzaghi, T., Xanthopoulos, P. and Seref, O., 2015. Constraint relaxation, cost-sensitive learning and bagging for imbalanced classification problems with outliers. Optimization Letters, pp.1-14.
- Razzaghi, T. and Safro, I., 2015. Scalable multilevel support vector machines. Procedia Computer Science, 51, pp.2683-2687.
- Razzaghi, T., Roderick, O., Safro, I. and Marko, N., 2015, July. Fast imbalanced classification of healthcare data with missing values. In Information Fusion (Fusion), 2015 18th International Conference on (pp. 774-781). IEEE.
- Xanthopoulos, P. and Razzaghi, T., 2014. A weighted support vector machine method for control chart pattern recognition. Computers & Industrial Engineering, 70, pp.134-149.
- Seref, O., Razzaghi, T. and Xanthopoulos, P., 2014. Weighted relaxed support vector machines. Annals of Operations Research, pp.1-37.
- IE 351 Applied Problem Solving in Industrial Engineering