Research

Publications

1. Simpson, A., Michael, S., Borchert, D., Saunders, C., and Tang, L. (2023) Modeling subpopulations for hierarchically structured data. Accepted by Statistical Analysis and Data Mining

2. Beshera, K. A., Harris, P. M., & Michael, S. (2023). Relative Genetic Homogeneity within a Phenotypically Diverse group: The Case of Lake Tana Labeobarbus (Cyprinidae) Species Flock, Ethiopia. Zootaxa, 5301(2), 182-198.

3. Schulz, M., Spors, E., Bates, K., & Michael, S. (2022). Spatial analysis of breast cancer mortality rates in a rural state. Preventing Chronic Disease, 19, E65.

4. Gardezi, M., Michael, S., Stock, R., Vij, S., Ogunyiola, A., & Ishtiaque, A. (2022). Prioritizing climate‐smart agriculture: An organizational and temporal review. Wiley Interdisciplinary Reviews: Climate Change, 13(2), e755.

5. Ngorsuraches, S., Michael, S., Poudel, N., Djira, G., Griese, E., Selya, A., & Da Rosa, P. (2021). Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study. Journal of Patient Experience, 8, 2374373520981480.

6. Michael, S., Miljkovic, T., & Melnykov, V. (2020). Mixture modeling of data with multiple partial right-censoring levels. Advances in Data Analysis and Classification, 14, 355-378.

7. Melnykov, V. and Michael, S. (2020) Clustering Large Datasets by Merging K-Means Solutions. Journal of Classification doi:10.1007/s00357-019-09314-8, 

8. Fosu, M., Gupta, T. and Michael, S. (2019)S ocial Media in Chemistry: Using a Learning Management System and Twitter To Improve Student Perceptions and Performance in Chemistry. In: T. Gupta and R. Belford (Ed.), Technology Integration in Chemistry Education and Research (TICER), American Chemical Society Symposium Series; ch. 13, pp. 185–208. doi:10.1021/bk-2019-1318.ch013

9.  Abdalla, A. and Michael, S. (2019) Finite mixture of regression models for a stratified sample. Journal of Statistical Computation and Simulation 89 (14) 2782–2800 doi:10.1080/00949655.2019.1636990, 2019

10. Holzhauser, C., Da Rosa, Patricia, and Michael, S. (2019) Forecasting participants in the All Women Count! mammography program. Preventing Chronic Disease 15: E150 doi:10.5888/pcd15.180177, 2018

11. Bajracharya, A. Khan, R., Michael, S., and Tonkoski, R. (2018) Forecasting Data Center Load Using Hidden Markov Model. North American Power Symposium 1-5 doi:10.1109/NAPS.2018.8600677, 2018

12. Ngorsuraches, S., Da Rosa, P., Ge, X., Djira, G., Michael, S., Wey, H. (2018) Patient Engagement as a predictor for health outcomes and cost in multiple chronic conditions. Value in Health 21, S88-S89

13. Shakya, A., Michael, S., Saunders, C., Armstrong, D., Pandey, P., Chalise, S., Tonkoski, R. (2017) Solar Irradiance Forecasting in Remote Microgrids using Markov Switching Model,  IEEE Transactions on Sustainable Energy 8 (3), 895-905 doi:10.1109/TSTE.2016.2629974

14. Michael, S. and Melnykov, V. (2016) Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology, Journal of Classification 33 (3), 412-441, 2016

15. Shakya, A., Michael, S., Saunders, C., Armstrong, D., Pandey, P., Chalise, S., and Tonkonski R. (2016) Solar Irradiance Forecasting in Remote Microgrids using Markov Switching Model. In 2016 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 1-7). IEEE.

16. Michael, S. and Melnykov, V. (2016) An effective strategy for initializing the EM algorithm in finite mixture models Advances in Data Analysis and Classification 10 (4), 563-583

17. Melnykov, V., Melnykov, I., and  Michael, S., (2015) Semi-supervised model-based clustering with positive and negative constraints V Melnykov, I Melnykov,  and S Michael, Advances in Data Analysis and Classification, 1-23, 2015

18. Melnykov, V., Michael, S., and Melnykov, I. (2014) Recent Developments in Model-Based Clustering with Applications V Melnykov, S Michael, and I Melnykov Partitional Clustering Algorithms, 1-39

19. Michael, S. and Melnykov, V. (2014) Studying complexity of model-based clustering. Communications in Statistics-Simulation and Computation 

Grants

1. PI, "Developing Explainable Machine Learning and Computational Methods for Identifying Geographic and Racial Disparities in End Stage Renal Disease" (Award No.  1OT2OD032581-01, OTA-21-271). Co-PI's: Varilek, B., Moradi Rekabdarkolaee, H., Ngorsuraches, S., & Brooks, P. National Institute of Health Office of Data Science Strategy's AIM-AHEAD Consortium Development Grant. Amount awarded $1,091,316.00 (9/2023 -  9/2025). 

2. Statistician, "Culturally responsive palliative care messaging for American Indians: An efficacy trial" (Award No. 1R21NR020383) Isaacson, M. (PI), and Anderson, J. (PI). National Institute of Nursing Research of the National Institutes of Health. Amount awarded: $368,794 (2022 – 2023).

3. Senior Personnel, “Testing a responsible innovation approach for integrating precision Agriculture (PA) technologies with future farm workers and work", Award # 2026431 Gardezi, M. (PI) Amount awarded: $2,996,759.00 National Science Foundation (Sept, 2020 – Aug, 2024).

4. Subaward Co-PI “Accounting for Covariates in Forensic Error Rate Assessment and Evidence Interpretation”, Saunders, C. (PI- SDSU), Tang, L (PI-UCF) Amount awarded: 108,104.00 National Institute of Justice (Sept 2020- Apr 2022)

5. Co-PI “Anticipating Risks and Benefits of Precision Agriculture (PA) for the Future of Agricultural Work and Workforce: A Multi-Stakeholder Research Agenda." Gardezi, M. (PI) Amount awarded: $150,000 National Science Foundation. Funded. (Sept, 2019 - Aug, 2020)

6. Co-PI “MRI: Acquisition of a Microgrid Cyber-Physical Testbed for Advanced Energy Management Systems," National Science Foundation, Tonkoski, R. (PI) Amount awarded: $360,516 (Sept, 2017 -Aug, 2020)

7. PI "E-commerce analytics - finite mixture modeling for applications in E-commerce," Arnold K. Skeie - foundation, Amount awarded: $158,590 (Dec 2016 - Dec 2019).

8. PI "Topic Modeling for the Daschle Collection,” College of Arts and Science and SDSU Foundation, South Dakota State University, Amount awarded: $8253.21 (May - Aug 2017).

9. PI “Combining structured and unstructured data for predictive modeling," SDSU faculty excellence grant, Amount awarded: $7,000 (Jan 2018 - Jun 2018).

10. PI "Natural language processing of text in the patient satisfaction survey," Sanford Health, Amount awarded: $1,000 (Jan 2018 - Jun 2018).

11. PI "Breast cancer screening disparities in South Dakota," Women and giving, SDSU Foundation, Amount awarded: $1,000 (Jan 2018 - May 2018).

12. CO-PI "Patient Engagement in the Management of Multiple Chronic Conditions," Sponsored by Sanford Health, Regional, Ngorsuraches, S. (PI) Amount awarded: $1,000.00. (Dec 2016 – Jun 2017).