Publications:

1. Bennett, R.*, Pierce, S., and Razzaghi, T. (2025). "Interpretable Machine Learning Models for Predicting Cesarean Delivery in Class III Obese Cohorts.'' IEEE Access.

2. Kolli, R.*, Razzaghi T., Pierce, S., Edwards, R., Maxted, M., and Parikh, P. (2023), "Predicting cesarean delivery amongst morbidly obese gravidas-a machine learning approach." AJOG Global Reports, 3(4), 100276. https://doi.org/10.1016/j.xagr.2023.100276 

3. Bennett, R.*, Mulla, Z., Parikh, P., Hauspurg, A., and Razzaghi, T. (2022). "An Imbalance-aware Deep Neural Network for Early Prediction of Preeclampsia.'' PLOS ONE, 17(4), e0266042. https://doi.org/10.1371/journal.pone.0266042 

Publications:

1. Derakhshi, M.*, and Razzaghi, T. (2024) "An Imbalance-aware BiLSTM for Control Chart Patterns Early Detection." Expert Systems with Applications, 249, 123682. https://doi.org/10.1016/j.eswa.2024.123682 

2. Okafor, P.*,  Siddique, Z.,  and Razzaghi, T. (2024) "Monitoring Recombinant Protein Titer in Escherichia Coli Fermentations Using Filtering Techniques." In 2024 IEEE International Conference on Big Data (BigData) (pp. 2197-2203). IEEE. 10.1109/BigData62323.2024.10825943

3. Polk, J.*, Huynh, L., Razzaghi, T., Derakhshi, M.*, and Wickham, S. (2024) "Optimizing Protein Titer Production Using Animal Cells: Predictive Modeling and Recommendations for Enhanced Yield." Conference Proceedings of 2024 IISE Annual Meeting, Montreal, Canada.

4. Cytovance Biologics, Razzaghi, T., (2024) "Advancing Biomanufacturing Excellence Through Artificial Intelligence," https://cytovance.com/news-articles/advancing-biomanufacturing-excellence-through-artificial-intelligence/ 

Publications:

1. Mohanty, S.*, Shimamura, A., Nicholson, C., Gonzáles, A., and Razzaghi, T. (2025) "Hierarchical Time Series Forecasting of COVID-19 Cases Using County-level Clustering Data." In Operations Research Forum (Vol. 6, No. 1, p. 28). Cham: Springer International Publishing. https://link.springer.com/article/10.1007/s43069-025-00424-1 

2. Unterbrink, J.**, Nicholson, C., Razzaghi, T., González, A., Velazco-Paredes, Y., and Huamani B, "Monitoring COVID-19 in Peru: Unraveling, Analyzing, and Recommendations." IISE Magazine, February, 2024.

3. Nicholson, C., Beattie, L.**, Beattie, M., Razzaghi, T., and Chen, S. (2022). "A Machine Learning and Clustering-based Approach for County-level COVID-19 Analysis." PLOS ONE, 17(4), e0267558. https://doi.org/10.1371/journal.pone.0267558 

Publications:

1. Fuqua, D.*, and Razzaghi, T. (2020). "A Cost-Sensitive Convolution Neural Network for Control Chart Pattern Recognition." Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.113275 

2. Razzaghi, T., Xanthopoulos, P., and Şeref, O. (2017). "Constraint Relaxation, Cost-sensitive Learning and Bagging for Imbalanced Classification Problems with Outliers." Optimization Letters. https://doi.org/10.1007/s11590-015-0934-z 

3. Seref, O., Razzaghi, T., and Xanthopoulos, P. (2017). "A Weighted Relaxed Support Vector Machine Method." Annals of Operations Research, 249(1-2), 235-271. https://doi.org/10.1007/s10479-014-1711-6