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Actuarial Science Program

Research Activities

I. Publications in SOA-designated journals:

  1. Flores Contro, J. M., Henshaw, K., Loke, S.-H., Constantinescu, C., & Arnold, S. (2024). Subsidising inclusive insurance to reduce poverty. North American Actuarial Journal.
  2. Xu, S., Manathunga, V., & Hong, D. (2023). Framework of BERT-based NLP models for frequency and severity in insurance claims. Variance, 16(2).
  3. Xiong, L., Luo, J., Vise, H., & White, M. (2023). Distributed least-squares Monte Carlo for American option pricing. Risks, 11(8), 145.
  4. Manathunga, V., & Deng, L. (2023). Pricing pandemic bonds under Hull–White & stochastic logistic growth model. Risks, 11(9), 155.
  5. Xiong, L., Manathunga, V., Luo, J., Dennison, N., Zhang, R., & Xiang, Z. (2023). AutoReserve: A web-based tool for personal auto insurance loss reserving with classical and machine learning methods. Risks, 11(7), 131.
  6. Xu, S., Zhang, C., & Hong, D. (2022). BERT-based NLP techniques for classification and severity modeling in basic warranty data study. Insurance: Mathematics and Economics, 107, 57–67.
  7. Loke, S.-H., & Thomann, E. (2018). Numerical ruin probability in the dual risk model with risk-free investments. Risks, 6(4), 110.
  8. Avram, F., & Loke, S.-H. (2018). On central branch/reinsurance risk networks: Exact results and heuristics. Risks, 6(2), 35.
  9. Landriault, D., Li, B., Loke, S.-H., Willmot, G. E., & Xu, D. (2017). A note on the convexity of ruin probabilities. Insurance: Mathematics and Economics, 74, 1–6.

II. Actuarial Science-related research published in other journals:

  1. Liu, Y., Yang, L., Xiong, L. (2023). Performance commitments and the properties of analyst earnings forecasts: Evidence from Chinese reverse merger firms. International Review of Financial Analysis, 89, 102775.
  2. Xiong, L., & Hong, D. (2022). CapSolve: A solvency assessment and prediction framework for workers’ compensation captive insurance companies. Journal of Insurance Issues, 45(2), 82–113.
  3. Manathunga, V., & Zhu, D. (2022). Unearned premium risk and machine learning techniques. Frontiers in Applied Mathematics and Statistics, 118.
  4. Wang, D., Hong, D., & Wu, Q. (2022). Prediction of loan rate for mortgage data: Deep learning versus robust regression. Computational Economics.
  5. Chen, Y., & Khaliq, A. Q. M. (2022). Comparative study of mortality rate prediction using data-driven recurrent neural networks and the Lee–Carter model. Big Data and Cognitive Computing, 6(4), 134.
  6. Cohen, A., & Loke, S.-H. (2022). So you want to price and invest in options? In Mathematics Research for the Beginning Student, Volume 2: Accessible Projects for Students After Calculus (pp. 101–125). Springer International Publishing.
  7. Feng, R., Garrido, J., Jin, L., Loke, S.-H., & Zhang, L. (2022). Epidemic compartmental models and their insurance applications. In Pandemics: Insurance and Social Protection (pp. 13–40). Springer.
  8. Tour, G., Thakoor, N., Khaliq, A. Q. M., & Tangman, D. Y. (2018). COS method for option pricing under a regime-switching model with time-changed Lévy processes. Quantitative Finance, 18(4), 673–692.
  9. Yin, L., Wu, Q., & Hong, D. (2016). Statistical methods for medical trend analysis in health rate review process. Journal of Health & Medical Informatics, 7, 219.

III. Other actuarial-related research publications:

  1. Long, J., & Khaliq, A. Q. M. (2025). Parameter identification for PDEs using sparse interior data and a recurrent neural network. Scientific Reports. (In press)
  2. Oluwasakin, E. O., Khaliq, A. Q. M., & Furati, K. M. (2025). Learning time-varying parameters of stiff dynamical systems using physics-informed transfer neural network. Mathematics and Computers in Simulation, 238, 82–102.
  3. Honain, A. H., Furati, K. M., Sarumi, I. O., & Khaliq, A. Q. M. (2025). Generalized exponential time differencing for fractional oscillation models. Journal of Computational and Applied Mathematics, 461, 116456.
  4. Sarumi, I. O., Furati, K. M., & Khaliq, A. Q. M. (2025). Efficient second-order accurate exponential time differencing for time-fractional advection–diffusion–reaction equations with variable coefficients. Mathematics and Computers in Simulation, 230, 20–38.
  5. Long, J., Khaliq, A. Q. M., & Xu, Y. (2024). Physics-informed encoder-decoder gated recurrent neural network for solving time-dependent PDEs. Journal of Machine Learning for Modeling and Computing, 5(3), 69–85.
  6. Chen, Y., & Khaliq, A. Q. M. (2024). Quantum recurrent neural networks: Predicting the dynamics of oscillatory and chaotic systems. Algorithms, 17(4), 163.
  7. Hansun, S., Putri, F. P., Khaliq, A. Q. M., & Hugeng, H. (2022). On searching the best mode for forex forecasting: Bidirectional long short-term memory default mode is not enough. IAES International Journal of Artificial Intelligence, 11(4), 1596–1606.
  8. Hansun, S., Wickson, A., & Khaliq, A. Q. M. (2022). Multivariate cryptocurrency prediction: Comparative analysis of three recurrent neural networks approaches. Journal of Big Data, 9, 50.
  9. Xiong, L., Sun, T., & Green, R. (2022). Predictive analytics for 30-day hospital readmissions. Mathematical Foundations of Computing, 5(2), 93.
  10. Xiong, L. (2022). Predictive modeling for Transportation Security Administration claims data. ANWESH: International Journal of Management & Information Technology, 7(2), 10–20.
  11. Xiong, L., & Williams, S. D. (2022). Generalized linear model for predicting the credit card default payment risk. Advances in Science, Technology and Engineering Systems Journal. Special Issue on Innovation in Computing, Engineering Science & Technology.
  12. Xiong, L. (2020). Comparative study of predictive analytics algorithms and tools on property and casualty insurance solvency prediction. In Proceedings of the 4th International Conference on Business and Information Management (pp. 81–88).
  13. Yousuf, M., & Khaliq, A. Q. M. (2021). Partial differential integral equation model for pricing American option under multi-state regime switching with jumps. Numerical Methods for Partial Differential Equations.
  14. Yousuf, M., Khaliq, A. Q. M., & Alrabeei, S. (2018). Solving complex PIDE systems for pricing American option under multi-state regime switching jump–diffusion model. Computers & Mathematics with Applications, 75(8), 2989–3001.
  15. Lay, H. A., Colgin, Z., Reshniak, V., & Khaliq, A. Q. M. (2018). On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters. Monte Carlo Methods and Applications, 24(4), 309–321.

IV. Actuarial Science Faculty Grant Involvement/Leadership 

  1. NRT-QISE-AI: Middle Tennessee Interdisciplinary Graduate Research and Training in Quantum Information Science and AI Funding Amount: US $2,000,000 | Duration: 2025–2029 PI: Wandi Ding | Co-PIs: Abdul Khaliq, Hanna Teleska, Josh Philip, Jing Kong
  2. Secure Actuarial Data Collaboration Engine (SCALE) using Federated Learning, Zero Knowledge Proofs, and Encryption Techniques Funding Amount: $17,980 | Period: 2024–2025 PIs: Lu Xiong, David Koegel | Sponsor: Society of Actuaries & Casualty Actuarial Society | Competition: 2025 Individual Grant
  3. 2024 SIAM–Simons Undergraduate Summer Research Program Funding Amount: $33,648 | Date: June 2024 Co-PI: Sooie-Hoe Loke | Sponsors: Society for Industrial and Applied Mathematics & Simons Foundation
  4. DSI Seed Grant: Analyzing Legal Opinions on Business Insurance Cases During COVID-19 Funding Amount: $5,000 | Date: October 2024 PI: Vajira Manathunga | Sponsor: Data Science Institute, Middle Tennessee State University
  5. DSI Seed Grant: InsuerBERT – A Pre-trained Actuarial and Insurance Language Representation Model for Insurance and Actuarial Science Text Analytics Funding Amount: $5,000 | Date: January 2024 PI: Vajira Manathunga | Sponsor: Data Science Institute, Middle Tennessee State University
  6. NLP and Other AI Techniques for Applications in Actuarial Science Funding Amount: $15,000 | Period: 2021–2022 PIs: Don Hong, Vajira Asanka Manathunga, Qiang Wu, Lu Xiong | Sponsor: Society of Actuaries & Casualty Actuarial Society | Competition: 2021 Individual Grant
  7. Pandemic, Infection Disease Models and Insurance Applications Funding Amount: $18,280 | Date: July 2021 PIs: Sooie-Hoe Loke, Runhuan Feng | Sponsor: Casualty Actuarial Society (CAS) Individual Grants Competition
  8. Healthcare Data Integration Based on HL7 Technology Funding Amount: $42,000 | Period: 2021–2023 PI: Lu Xiong | Sponsor: MITEM
  9. Pandemic Bond Pricing Using Epidemic Compartment Models Funding Amount: $9,950 | Period: Jan 1, 2022 – Dec 31, 2022 PI: Vajira A. Manathunga | Co-PI: L. Deng | Sponsor: Office of Research and Sponsored Programs, Middle Tennessee State University
  10. Open Educational Resources for Actuarial Science (Spring 2022 OER Mini-grant) Funding Amount: $3,250 | Period: Jan 15, 2022 – Mar 31, 2022 PI: Vajira A. Manathunga | Co-PI: H. Pan | Sponsor: OER Steering Committee, Middle Tennessee State University
  11. Open Educational Resources for Actuarial Science (CBAS OER Grant) Funding Amount: $3,000 | Date Submitted: Nov 18, 2020 PIs: Vajira A. Manathunga, Lu Xiong | Co-PIs: Don Hong, Qiang Wu | Sponsor: CBAS, Middle Tennessee State University
  12. Actuarial Applications of Infection Age-Structured Epidemic Models Funding Amount: $10,000 | Date: Dec 2020 PI: Sooie-Hoe Loke | Sponsor: Canadian Institute of Actuaries (CIA–ICA) COVID-19 Long Term Impact Grant
  13. Excess Credibility in Mixed Parametric Probability Models Using Machine Learning Funding Amount: $20,000 | Date: June 2020 PI: Sooie-Hoe Loke et al. | Sponsor: Casualty Actuarial Society (CAS) Reinsurance Research Grant
  14. Central Convergence Research Experience for Undergraduates Funding Amount: $473,353 | Date: Aug 2020 PIs: Brandy Wiegers, Sooie-Hoe Loke | Sponsor: National Science Foundation (NSF–DMS 2050692)
  15. Predicting 30-day Hospital Readmission Using Machine Learning Techniques Funding Amount: $6,500 | Date: April 2019 PI: Lu Xiong | Sponsor: FRCAC, Middle Tennessee State University
  16. National Research Experience for Undergraduates Program (NREUP) Grant Funding Amount: ∼$27,500 | Date: Mar 2018 PIs: Brandy Wiegers, Sooie-Hoe Loke | Sponsor: Mathematical Association of America (MAA)
  17. Center for Undergraduate Research in Mathematics (CURM) Mini-grant Funding Amount: ∼$19,000 | Date: Feb 2018 PI: Sooie-Hoe Loke
  18. State of Tennessee Health Rate Review Project (Cycle I) Funding Amount: $300,000 | Period: 2011–2012 PI: Don Hong | Sponsor: Tennessee Department of Commerce and Insurance, funded by Health and Human Services (HHS)
  19. State of Tennessee Health Rate Review Project (Cycle II) Funding Amount: $440,000 | Period: 2011–2013 PI: Don Hong | Sponsor: Tennessee Department of Commerce and Insurance, funded by Health and Human Services (HHS)

V. Actuarial Science-related presentations:

a. ARC Presentations
  1. Manathunga, V., & Doan, H. D. (2025, August). Workers’ compensation case outcomes and large language models. 60th Actuarial Research Conference, York University, Toronto, Canada.
  2. Xiong, L., & Luo, J. (2024, July). Unraveling the complexities of urban housing market trends: A predictive analytics approach. 59th Actuarial Research Conference, Middle Tennessee State University, Murfreesboro, TN.
  3. Manathunga, V., & Deng, L. (2023, August). Pandemic bonds and stochastic logistic growth model. 58th Actuarial Research Conference, Drake University, Des Moines, IA.
  4. Manathunga, V., & Xu, S. (2022, August). Framework for BERT-based NLP models with applications to warranty policy. 57th Actuarial Research Conference.
  5. Chen, Y., & Khaliq, A. Q. M. (2022, August). Data-driven LSTM method to predict mortality under COVID-19 in the United States based on deep learning. 57th Actuarial Research Conference.
  6. Xiong, L. (2021, August). Reducing the runtime of least squares Monte Carlo in risk management. 56th Actuarial Research Conference.
  7. Xiong, L. (2019, August). Comparative study of predictive analytics algorithms and tools on property & casualty insurance solvency prediction. 54th Actuarial Research Conference.
  8. Fernando, K., & Manathunga, V. (2019, August). Modeling HPI price index using HJM approach. 54th Actuarial Research Conference, Indianapolis, IN.
  9. Carpenetti, B. (2015, August 5–8). Practical applications for medical trend analysis and health rating. 50th Actuarial Research Conference, Toronto, Canada.
  10. Page, K. (2015, August 5–8). Predictive analytics for minor league baseball pitchers. 50th Actuarial Research Conference, Toronto, Canada.
  11. Xiong, L. (2014, July). Using Monte Carlo simulation to predict captive solvency. 49th Actuarial Research Conference, University of California, Santa Barbara, CA.
  12. Ye, Y. (2013, July). Trend analysis algorithms and applications to health rate review. 48th Actuarial Research Conference, Temple University, Philadelphia, PA.
b. Other Presentations
  1. Manathunga, V., & Doan, H. D. (2025, August). Comparative analysis of workers’ compensation commission decisions using large language models. Joint Statistical Meeting, Nashville, TN.
  2. Xiong, L. (2024, February). Integrating health care data with HL7: Leveraging Google Cloud and RESTful APIs for enhanced interoperability. CDS Seminar, Middle Tennessee State University, Murfreesboro, TN.
  3. Hong, D. (2020, October). CAS University Award report on MTSU actuarial science program. CAS Annual Meeting (Remote).
  4. Xiong, L., & Hong, D. (2020). Using Monte Carlo simulation to predict captive insurance solvency. 4th International Conference on Compute and Data Analysis.
  5. Kazi, H. A., Manathunga, V., & Yantz, J. (2019, June). Challenges and successes of launching a new actuarial science program. Actuarial Teaching Conference, Columbus, OH.
  6. Fernando, K., & Manathunga, V. (2019, November 29–30). American option pricing under additive and multiplicative models using HJM approach. 2019 International Workshop on Actuarial Science and Finance, Ningbo University, China.
  7. Hong, D. (2018). Statistical learning and predictive analytics with applications in actuarial science. Invited talk at the 2018 International Workshop on Actuarial Science and Mathematical Finance, Ningbo, China.

VI. Graduate Student Thesis/Dissertation:

  1. Oluwasakin, E. O. (2024, August). Data-driven deep learning algorithms for dynamical systems (Doctoral dissertation, Dissertation Supervisor: Abdul Khaliq).
  2. Long, J. (2024, August). Sparse data deep learning algorithm for multidimensional partial differential equations (Doctoral dissertation, Dissertation Supervisor: Abdul Khaliq).
  3. Xu, S. (2021, October). Applications of modern NLP techniques for predictive modeling in actuarial science (Doctoral dissertation, Dissertation Supervisors: Don Hong & Sal Barbosa).
  4. Zhang, C. (2021, June). Aggregate loss prediction using multiple-class classification techniques (Master’s thesis, Thesis Supervisor: Don Hong). Middle Tennessee State University, Murfreesboro, TN.
  5. Xu, Y. (2020, August). Propensity score methods for comparing the effect of RHC on survival time (Master’s thesis, Thesis Supervisor: Yeqian Liu). Middle Tennessee State University, Murfreesboro, TN.
  6. Fang, Y. (2018, May). Predictive models for air show ticket sales (Master’s thesis, Thesis Supervisor: Don Hong). Middle Tennessee State University, Murfreesboro, TN.
  7. Matthews, D. (2016, May). Data mining and machine learning algorithms for workers’ compensation early severity prediction (Master’s thesis, Thesis Supervisor: Don Hong). Middle Tennessee State University, Murfreesboro, TN.
  8. Xiong, L. (2014, December). Statistical computing tools for predicting captive solvency (Doctoral dissertation chapter 3, Dissertation Supervisor: Don Hong).
  9. Ye, Y. (2014, August). Tail conditional expectations for extended dispersion models (Master’s thesis, Thesis Supervisor: Qiang Wu). Middle Tennessee State University, Murfreesboro, TN.
  10. Yin, L. (2014, May). Medical trend analysis methods (Master’s thesis, Thesis Supervisor: Qiang Wu). Middle Tennessee State University, Murfreesboro, TN.

VII. Funded Undergraduate Research:  

  1. Cao, X., Zhu, P., & Zhao, M. (2023, Spring). Tree-based machine learning algorithms for analytics of online shopper’s purchasing intention (MTSU URECA grant, $3,000). Faculty mentor: Lu Xiong.
  2. Zhang, Z., & Duan, Y. (2022, Fall). Actuarial modeling for medical loss prediction and trend analysis (MTSU URECA grant, $2,000). Faculty mentors: Don Hong, Shuzhe Xu.
  3. Hua, X. (2022, Spring). Investigating two-parameter composite models and their applications in actuarial science (MTSU URECA grant, $1,000). Faculty mentor: Vajira Manathunga.
  4. Zhang, J. (2022, Spring). Application of machine learning techniques for insurance fraud detection (MTSU URECA grant, $1,000). Faculty mentor: Don Hong.

VIII. Student Presentations:

  1. Sun, T. (2022, March). Distributed regression version of least squares Monte Carlo algorithm with Map-Reduce and GPU acceleration [Poster presentation]. MTSU Scholar Week, Middle Tennessee State University, Murfreesboro, TN. Faculty mentor: Lu Xiong.
  2. Zhang, J. (2020, March). Application of machine learning techniques for insurance fraud detection [Poster presentation]. MTSU Scholar Week, Middle Tennessee State University, Murfreesboro, TN. Faculty mentor: Don Hong.
  3. Sun, T. (2020, March). Using the automated machine learning to predict 30-day hospital readmission [Poster presentation]. MTSU Scholar Week, Middle Tennessee State University, Murfreesboro, TN. Faculty mentor: Lu Xiong.
  4. Zhang, C. (2018, March). Research about loss reserving method in P&C insurance [Poster presentation]. MTSU Scholar Week, Middle Tennessee State University, Murfreesboro, TN. Faculty mentor: Don Hong.