Faculty Profiles

Shaw, L., & Ekin, T. (n.d.). A Machine Learning Approach for Anomaly Detection in COVID-19 PCR Test Results Using Isolation Forests.

Shaw, L., Ansari, M. W., & Ekin, T. (n.d.). Adversarial Natural Language Processing: Overview, Challenges and Future Directions.

Caballero, W. N., Camacho Rodriguez, J. M., Ekin, T., & Naveiro, R. (2024). Manipulating Hidden Markov Model Inferences by Corrupting Batch Data. Computers and Operations Research, 162(February 2024).

Kumaraswamy, N., Ekin, T., Park, C., Markey, M. K., Barner, J. C., & Rascati, K. (2024). Using a Bayesian Belief Network to detect healthcare fraud. Expert Systems with Applications, 238.

Shaw, L., Ansari, W., & Ekin, T. (n.d.). Adversarial Natural Language Processing: Overview, Challenges and Future Directions. In Proceedings of 58th Hawaii International Conference on System Sciences. (pp. 1–10).

Ekin, T., Shaw, L., & Bellamkonda, S. (n.d.). Adversarial Attacks and Data-Driven Dynamic Outlier Detection Systems. In 5th International Conference on InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS 2024).

Shaw, L., Hebli, P., & Ekin, T. (2024). Siamese Networks and Adversarial Attacks: An Overview. In 2024 IEEE International Conference on Prognostics and Health Management.

Ekin, T., Naveiro, R., Barran, A. T., & Rios Insua, D. (2023). Augmented Probability Simulation Methods for Sequential Games. European Journal of Operational Research, 306(1), 418-430.

Ekin, T., Damien, P., & Walker, S. (2023). Augmented simulation methods for discrete stochastic optimization with recourse. Annals of Operations Research, 320, 771–793.

Ekin, T. (2023). Adversarial Outlier Detection for Health Care Fraud. In AMCIS 2023 (Vol. 2023). Panama City, Panama, AIS.

Ekin, T., & Garega, V. (2023). Command and control with poisoned temporal batch data. In 2023 SPIE Proceedings of Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V (Vol. 12538, pp. 73–81).

Ekin, T., & Musal, R. M. (2022). Integrated statistical and decision models for multi-stage health care audit sampling. Journal of Applied Statistics, 49(9), 2307–2325.

Zafari, B., Ekin, T., & Ruggeri, F. (2022). Multicriteria Decision Frontiers for Prescription Anomaly Detection Over Time. Journal of Applied Statistics, 49, 3638–3658.

Ekin, T. (2022). Forecasting with Perturbed Data.

Ekin, T., Naveiro, R., & Camacho Rodriguez, J. M. (n.d.). Adversarial Forecasting through Adversarial Risk Analysis within a DDDAS Framework.

Kumaraswamy, N., Markey, M., Ekin, T., Barner, J., & Rascati, K. (2022). Healthcare fraud data mining methods – A look back and look ahead. Perspectives in Health Information Management, 19(1), 1i.

Ekin, T., & Aktekin, T. (2021). Decision Making under Uncertain and Dependent System Rates in Service Systems. European Journal of Operational Research, 291(1), 335–348.

Ekin, T., & Damien, P. (2021). Analysis of Health Care Billing via Quantile Variable Selection Models. Healthcare, 9(10).

Ekin, T., & Lakomski, G. R. (2021). Risk based Payment Integrity Evaluation. In Proceedings of 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) (pp. 1–6). Sakheer, Bahrain: IEEE. https://doi.org/doi: 10.1109/ICDABI51230.2020.9325603

Ekin, T., Puggioni, G., & Canas Rodrigues, P. (2021). Foreword: Special issue of ASMBI for y‐BIS 2019: Recent Advances in Business Analytics and Data Science. Applied Stochastic Models in Business and Industry, 37(2), 157–158.

Fulton, L. V., Adepoju, L., Dolezel, D. M., Ekin, T., Gibbs, D. L., Hewitt, B. A., … Woodard, L. (2021). Determinants of Diabetes Disease Management, 2011-2019. Healthcare, 9(994).

Fulton, L. V., Adepoju, L., Dolezel, D. M., Ekin, T., Gibbs, D. L., Hewitt, B. A., … Woodard, L. (2021). Determinants of Diabetes Disease Management, 2011-2019. Healthcare, 9(8), 944. https://doi.org/10.3390/healthcare9080944

Fulton, L. V., Adepoju, L., Dolezel, D. M., Ekin, T., Gibbs, D. L., Hewitt, B. A., … Woodard, L. (2021). Determinants of Diabetes Disease Management, 2011-2019. Healthcare, 9(994).

Ekin, T., Frigau, L., & Conversano, C. (2021). Health Care Fraud Classifiers in Practice. Applied Stochastic Models in Business and Industry, 37(6), 1182–1199.

Lopez Perez, F., Ekin, T., Mendez, F., & Jimenez, J. (2020). Risk Balanced Territory Design for a Micro Finance Institution. Journal of Industrial and Management Optimization, 16(2), 741–758.

Ekin, T., Zarnikau, J., & Damien, P. (2020). Estimating marginal effects of key factors that influence electricity demand and price distributions in Texas via quantile variable selection methods. Journal of Energy Markets, 13(1), 1–29. https://doi.org/10.21314/JEM.2020.202

Ekin, T. (2020). Discussion of “Machine Learning in Nonlife Insurance.” Applied Stochastic Models in Business and Industry, 36, 541–544. https://doi.org/10.1002/asmb.2558

Konur, D., & Ekin, T. (2020). Make-to-Order Production Planning with Uncertain Quality (pp. 212–219). International Conference on Information Systems, Logistics and Supply Chain. Retrieved from https://www.scopus.com/record/display.uri?eid=2-s2.0-85085939768&origin=resultslist

Zafari, B., & Ekin, T. (2019). Topic Modelling for Medical Prescription Fraud and Abuse Detection. Journal of the Royal Statistical Society. Series C: Applied Statistics, 68, 751–769.

Ekin, T. (2019). Statistics and Health Care Fraud: How to Save Billions. Boca Raton, FL.: Chapman Hall/CRC Press. ASA-CRC Series on Statistical Reasoning in Science and Society. Retrieved from https://www.crcpress.com/Statistics-and-Health-Care-Fraud-How-to-Save-Billions/Ekin/p/book/9781138197428

Ekin, T., Lakomski, G., & Musal, R. (2019). An Unsupervised Bayesian Hierarchical Method for Medical Fraud Assessment. Statistical Analysis and Data Mining, 12, 116–124.

Ekin, T. (2019). Fraud Analytics. Proceedings of Y-BIS 2019 Conference: Recent Advances in Data Science and Business Analytics, 33.

Ekin, T. (2019). An Integrated Decision-making Framework for Medical Audit Sampling (pp. 4107–4114). Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS52).

Musal, R., & Ekin, T. (2018). Information Theoretic Multi-Stage Sampling Framework for Medical Audits. Applied Stochastic Models in Business and Industry, 34, 893--907.

Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2018). Statistical Medical Fraud Assessment: An Exposition to an Emerging Field. International Statistical Review, 86, 379--402.

Ekin, T. (2018). Integrated Maintenance and Production Planning with Endogenous Uncertain Yield. Reliability Engineering and System Safety, 179(November), 52–61.

Zafari, B., & Ekin, T. (2018). Topic Modelling for Medical Prescription Fraud and Abuse Detection. In Proceedings of 13th INFORMS Workshop on Data Mining and Health Informatics.

White, G., Ekin, T., & Visinescu, L. (2017). Analysis of Protective Behavior and Security Incidents for Home Computers. Journal of Computer Information Systems, 57(4), 353–363.

Ekin, T., Polson, N. G., & Soyer, R. (2017). Augmented Nested Sampling for Stochastic Programs with Recourse and Endogenous Uncertainty. Naval Research Logistics, 64(8), 613--627.

Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2017). On the Use of the Concentration Function in Medical Fraud Assessment. The American Statistician, 71(3), 236–241.

Musal, R., & Ekin, T. (2017). Medical Overpayment Estimation: A Bayesian Approach. Statistical Modelling, 17(3), 196–222.

Bastian, N. D., Ekin, T., Griffin, H. M., Griffin, P. M., Fulton, L. V., & Grannan, B. C. (2017). Stochastic Multi-Objective Auto-Optimization for Resource Allocation Decision-Making in Fixed-Input Health Systems. Health Care Management Science, 20(2), 246–264. https://doi.org/10.1007/s10729-015-9350-2 JCR IF: 2.1, JCR 5-Year IF: 2.4, AR: 19-20%

Ekin, T., Kocadagli, O., Bastian, N., Fulton, L. V., & Griffin, P. (2016). Fuzzy Decision-Making in Health Systems: A Resource Allocation Model. Euro Journal on Decision Processes, 4(3), 245–267.

Aktekin, T., & Ekin, T. (2016). Stochastic Call Center Staffing with Uncertain Arrival, Service and Abandonment Rates: A Bayesian Perspective. Naval Research Logistics, 63(6), 460–478.

Lopez, F., Ekin, T., Mendez, F., & Jimenez, J. (2015). Hybrid heuristic for dynamic location-allocation on micro-credit territory design. Computacion y Sistemas, 19(4), 783–804.

Ekin, T., Musal, R., & Fulton, L. (2015). Overpayment Models for Medical Audits: Multiple Scenarios. Journal of Applied Statistics, 42(11), 2391–2405.

Ekin, T., Musal, R., & Fulton, L. (2015). Overpayment Models for Medical Audits: Multiple Scenarios. Journal of Applied Statistics, 42(11), 2391–2405. https://doi.org/https://doi.org/10.1080/02664763.2015.1034659 JCR IF:  .8

Lopez, F., Ekin, T., Mendez, F., & Jimenez, J. (2015). Territory Design with Risk for a Micro Finance Institution. Texas State University Center for High Performance Systems.

Ekin, T., Polson, N., & Soyer, R. (2014). Augmented MCMC Simulation for Two-Stage Stochastic Programs with Recourse. Decision Analysis, 11(4), 250–264.

Bastian, N., Fulton, L. V., Shah, V., & Ekin, T. (2014). Resource allocation decision making in the military health system. IIE Transactions on Healthcare Systems Engineering, 4(2), 80–87.

Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2013). Bayesian Co-Clustering Methods for Assessment of Healthcare Fraud (4th ed., Vol. 2013). The George Washington University The Institute for Integrating Statistics in Decision Sciences.

Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2013). Application of Bayesian Methods in Detection of Healthcare Fraud. Chemical Engineering Transactions, 33, 151–156.

Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2013). Statistical Issues in Medical Fraud Assessment. Milano, Italy: MOX, Dipartimento di Matematica, Politecnico di Milano. Retrieved from mox.polimi.it/it/progetti/pubblicazioni/quaderni/28-2013.pdf

Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2013). Statistical Issues in Medical Fraud Assessment (11th ed., Vol. 2013). Washington, DC, United States: The George Washington University The Institute for Integrating Statistics in Decision Sciences. Retrieved from business.gwu.edu/decisionsciences/i2sds/pdf/TR-2013-11.pdf

Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2013). Application of Bayesian Methods in Healthcare Fraud (1st ed., Vol. 2013). Washington, DC, United States: The George Washington University The Institute for Integrating Statistics in Decision Sciences.

Ekin, T., Polson, N., & Soyer, R. (2012). Simulation-Based Two-Stage Stochastic Programming with Recourse (4th ed., Vol. 2012). The George Washington University The Institute for Integrating Statistics in Decision Sciences. Retrieved from http://business.gwu.edu/about-us/research/institute-for-integrating-statistics-in-decision-sciences/technical-reports/2012-2/

Ekin, T., Caglar, T., & Soyer, R. (2012). Bayesian Co-Clustering Methods in Healthcare Fraud Detection.