Publications

Accepted Refereed Journal Articles

  • Bennett, R., Razzaghi, T., Mulla, Z. (2022). “Early Prediction of Preeclampsia Using Machine Learning Methods.” PLOS ONE, 17(4), e0266042. View paper.Maternal HealthPredictive ModelingClass Imbalance
  • Bennett, R., Pierce, S., Razzaghi, T. (2025). “Interpretable Machine Learning Models for Predicting Cesarean Delivery in Class III Obese Cohorts.” IEEE Access, 13, 41230–41247. View paper · GitHub repository.Maternal HealthInterpretable MLClinical Prediction

Working papers/Submitted

  • Bennett, R., Razzaghi, T. “Optimizing Deep Neural Networks for Scalability, Class Balance, and Fairness: A Multilevel Graph Representation Approach.” Submitted to Expert Systems with Applications (under review).Deep LearningFairnessScalability
  • Bennett, R., Tah, L., Razzaghi, T. “Predicting Length of Stay in Preeclampsia: A Tabular-to-Text Machine Learning Approach for Imbalanced Data.” Submitted to Knowledge-Based Systems (under review).Maternal HealthLength of StayImbalanced Data
  • Bennett, R., Janitz, A., Noyd, D., Razzaghi, T. “An Interpretable Survival Machine Learning Framework for Detecting Suboptimal Care in Childhood Cancer Survivors.” Submitted to Healthcare Management Science (under review).Cancer SurvivorshipSurvival AnalysisInterpretable ML
  • Bennett, R., Janitz, A., Noyd, D., Razzaghi, T. “Predicting Suboptimal Care Among Childhood Cancer Survivors: A Zero-Inflated Survival Approach.” Working paper.Cancer SurvivorshipSurvival AnalysisHealthcare Access
  • Mohamed, A., Bennett, R., Razzaghi, T. “Interpretable Machine Learning Models for Early Detection of Preeclampsia.” Submitted to International Journal of Medical Informatics (under review).Maternal HealthInterpretable MLEarly Detection

Book Chapters

  • Bennett, R., Hemmati, M., Ramesh, R., Razzaghi, T. (2024). “Artificial Intelligence and Machine Learning in Precision Health: An Overview of Methods, Challenges, and Future Directions.” Dynamics of Disasters: From Natural Phenomena to Human Activity, 15–53. View chapter.Precision HealthAI in HealthcareSurvey

Conference Papers

  • Razzaghi, T., Bennett, R., Polk, J., Derakhshi, M., Le, H., Wickham, S. (2024). “Optimizing Protein Titer Production using Animal Cells: Predictive Modeling and Recommendations for Enhanced Yield.” 2024 IISE Annual Conference and Expo. View paper.BiomanufacturingPredictive ModelingOptimization

Presentations

Invited Talks

  • Using Predictive Modeling to Support Maternal Health and Cancer Survivorship; EN 591 Guest Lecture Series—AI for the Public Good; April 2026; Colorado State University, CO.
  • AI Assisted LaTeX 101: Writing, Formatting, and Debugging Faster; INFORMS Student Chapter; February 2026; Norman, OK.
  • Improving Maternal Health: Predicting Length of Stay in Preeclampsia with Imbalanced Learning; 2025 INFORMS Annual Conference; Atlanta, GA.
  • Fairness, Balance, and Scale: A Multilevel Graph Approach to Deep Neural Networks; 2025 INFORMS Annual Conference; Atlanta, GA.
  • Scalable and Trustworthy Deep Neural Networks for Imbalanced Data; 2024 INFORMS Annual Conference; Seattle, WA.
  • Multilevel Neural Networks for Robust Learning; 2024 IISE Annual Conference; Montréal, Canada.
  • An Adaptive Multilevel Neural Network for Early Detection of Preeclampsia; 2023 INFORMS Annual Conference; Phoenix, AZ.
  • Early Detection of Preeclampsia using a Scalable Deep Neural Network Algorithm; 2022 INFORMS Annual Conference; Indianapolis, IN.
  • Machine Learning Methods and Applications in Materials; 2022 ASME Central Oklahoma Section Lightning Talks; Norman, OK.
  • An Imbalance Aware Deep Neural Network for Early Detection of Preeclampsia; 2021 INFORMS Annual Conference; Anaheim, CA.
  • A Cost-sensitive Deep Neural Network Model for Preeclampsia Prediction; 2021 IISE Annual, Virtual Conference.
  • Predicting the Development of Preeclampsia using Cost-Sensitive Deep Neural Networks; 2021 ASME Central Oklahoma Section Lightning Talks; Norman, OK.
  • Machine Learning: A Brief Demo of Supervised Learning; University of Oklahoma Tutorial: INFORMS student organization; April 2023; Norman, OK.

Poster Presentations

  • Identifying Patterns of Suboptimal Follow-Up Care Among Childhood Cancer Survivors Using Survival Analysis Models; Innovating for Impact: DISC Inaugural Data Science Symposium; 2025; Norman, OK.
  • Fair Multilevel Neural Networks; INFORMS: Minority Issues Forum, 2024 INFORMS Annual Conference; Seattle, WA.
  • Fast Multilevel Neural Networks to Overcome Bias in Healthcare Applications; INFORMS: Minority Issues Forum, 2023 INFORMS Annual Conference; Phoenix, AZ.
  • Early Detection of Preeclampsia using a Scalable Deep Neural Network Algorithm; 2023 Oklahoma Conference for Statistics, Biostatistics, and Data Science; Oklahoma City, OK.
  • An Imbalance-Aware Deep Neural Network for Early Prediction of Preeclampsia; 2023 INFORMS Business Analytics; San Antonio, TX.
  • Predicting Preeclampsia Using Cost-Sensitive Deep Neural Networks; 2020 Graduate College of Engineering Grad Student Poster Fair; Norman, OK.
  • Determining the Volume of a Surface Defined by Tomographic Scattering Points; 2015 Mathematical Association of America MathFest; Washington, D.C.