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Department of Mathematics and Statistics

Semhar Michael

A portrait of Dr. Semhar Michael smiling

Title

Associate Professor of Statistics

Office Building

Chicoine Architecture, Mathematics and Engineering Hall

Office

264

Mailing Address

Chicoine Architecture, Math and Engineering Building 264
Math and Statistics-Box 2225
University Station
Brookings, SD 57007

Biography

I am an applied statistician by training. My research focuses on computational statistics with an emphasis on developing novel methodologies for analyzing datasets in challenging forms. I have addressed problems in clustering of time series, forensic and text data. My work has been published in peer-reviewed statistics journals and led to national paper competition awards. In the application side, I have developed a forecasting algorithm jointly with colleagues in electrical engineering. With unstructured data, I worked on a building clustering, classification and sentiment analysis models. In the health sciences, I have worked on mixture modeling, spatial clustering and forecasting projects on datasets from electronic health records and publicly available datasets.

Social Media Links

Education

  • B.S. in applied mathematics | University of Asmara, Eritrea | 2006
  • M.S. in mathematics | University of North Dakota | 2011
  • Ph.D. in applied statistics | University of Alabama | 2015

Academic Interests

  • Computational statistics
  • Mixture modeling
  • Unsupervised learning
  • Clustering
  • Unstructured data analysis
  • Forensic source identification problems

Academic Responsibilities

  • Fall 2024: Stat 684 Statistical Inference I and Stat 715 Multivariate Analysis
  • Spring 2023: Statistical Computation and Simulation
  • Spring 2021, 2022, 2023: STAT 685 Statistical Inference II
  • Fall 2021, 2022, 2023: STAT 715 Multivariate Statistics
  • Fall 2018, 2019, 2020: STAT 684 and 685: Statistical Inference I and II
  • Fall 2017: STAT 784 and 785: Statistical Inference I and II
  • Fall 2015-16: STAT 701: Modern Applied Statistics I
  • Spring 2017: STAT 702: Modern Applied Statistics II
  • Fall 2015 - Spring 2017: Independent studies on Natural language processing, Finite mixture modeling and model-based clustering, advanced computational analysis

Committees and Professional Memberships

Committees

  • Associate editor, Journal of Classification
  • Chair and member of the Organizing Committee, South Dakota State University Annual Data Science Symposium
  • Reviewer, Computational Statistics and Data Analysis, Journal of Computational and Graphical Statistics, Journal of Applied Statistics, Journal of Computation and Simulation, Computational Economics, Law Probability and Risk, etc.
  • Session chair: Topic Contributed Session of Joint Statistical Meeting (2015, 2017)
  • Session organizer and chair: Topic Contributed Session at Joint Statistical Meetings (2016)

Professional Memberships

  • American Statistical Association (ASA), member, 2012–present
  • Institute of Mathematical Statistics (IMS), member, 2012–present
  • International Institute for Analytics (IIA), member, 2013–present

Awards and Honors

  • 2017, Certificate of Appreciation (advising Damon Bayer and Eric Stratman), Van D. and Barbara B. Fishback Honors College
  • 2016, ASA Travel award for attending Women in Statistics and Data Science Conference
  • 2013-2015, Excellence in Research and Teaching, ISM Department at UA
  • 2014, R.L. Anderson student poster award Top 6 from 63 posters
  • 2014, 2013, Boyd Harshbarger travel fund, SRCOS
  • 2014, Outstanding Ph.D. Student in Applied Statistics, ISM Department at UA
  • 2013, Laha Travel Award
  • 2013, Annual National SAS Analytics and Data Mining Shootout Competition Winner

Grants

  • Michael, S. (Principal), Co-Principals: Saunders, C. (SDSU), Melnykov, Y. (UA) and May, P. (SDSMT) “ATD: Development of Statistical Methods for Detection and Characterization of Latent Subpopulations of Classes" $350,796. National Science Foundation (Sept. 2024-Aug. 2026).
  • Daraboina, R. (Principal) (SDSU) and Michael, S. (Co-Principal) et al., “Predicting Expanded Food and Nutrition Education Program Dropout Using Artificial Intelligence." 13, 057.00 Sponsored by Research, Scholarship and Creative Activity (RSCA) Challenge Fund, SDSU (July 2024-June 2025).
  • Michael, S. (PI), Co- PIs: Brandenburger, T. (SDSU) and Djira, G. (SDSU) “Conference: SDSU Data Science Symposium" $7,500. National Science Foundation (Feb. 2024-Aug. 2024).
  • Michael, S. (PI), Varilek, B., Moradi Rekabdarkolaee, H., Ngorsuraches, S. and Brooks, P. "Developing Explainable Machine Learning and Computational Methods for Identifying Geographic and Racial Disparities in End Stage Renal Disease." National Institute of Health Office of Data Science Strategy's AIM-AHEAD Consortium Development Grant, September 2023 - September 2025, $1,091,316.
  • Isaacson, M. and Anderson, J.  (Michael, S., Senior Statistician) "Culturally responsive palliative care messaging for American Indians: An efficacy trial." National Institute of Nursing Research of the National Institutes of Health, 2022-2023, $368,794.
  • Gardezi, M. et al. (Michael, S. - Senior Personnel) “Testing a responsible innovation approach for integrating precision Agriculture (PA) technologies with future farm workers and work". National Science Foundation, September 2020 - August 2024, $2,996,759. 
  • Saunders, C.  and Michael, S. (Co-PI), “Accounting for Covariates in Forensic Error Rate Assessment and Evidence Interpretation”. National Institute of Justice, September 2020 - April 2022, 108,104. (Tang, L. (PI) at UCF)
  • Gardezi, M. et al. Michael, S. (Co-PI) “Anticipating Risks and Benefits of Precision Agriculture (PA) for the Future of Agricultural Work and Workforce: A Multi-Stakeholder Research Agenda." National Science Foundation, September 2019 - August 2020, $150,000.
  • Tonkoski, R. et al. Michael, S.(Co-PI) “MRI: Acquisition of a Microgrid Cyber-Physical Testbed for Advanced Energy Management Systems." National Science Foundation, September 2017 - August 2020, $360,516.
  • Michael, S. "E-commerce analytics - finite mixture modeling for applications in E-commerce," Arnold K. Skeie - foundation, December 2016 - December 2019, $158,590.
  • Michael, S. "Topic Modeling for the Daschle Collection,” College of Arts and Science and SDSU Foundation, South Dakota State University, May - August 2017, $8,253.21.
  • Michael, S. “Combining structured and unstructured data for predictive modeling," SDSU faculty excellence grant, January-June 2018, $7,000.
  • Michael, S. "Natural language processing of text in the patient satisfaction survey." Sanford Data Collaborative, January-June 2018, $1,000.
  • Michael, S. "Breast cancer screening disparities in South Dakota."  Women and giving, SDSU Foundation, January-May 2018, $1,000.
  • Ngorsuraches, S. et al. and Michael, S. (Co-PI) "Patient Engagement in the Management of Multiple Chronic Conditions." Sanford Health, Regional, December 2016 - June 2017, $1,000.

Work Experience

  • 2021-present, associate professor, South Dakota State University
  • 2015-2021, assistant professor, South Dakota State University
  • 2009-2025, graduate teaching assistant, University of North Dakota, North Dakota State University

Areas of Research

  • Computational statistics
  • Mixture modeling
  • Unsupervised learning
  • Model-based clustering
  • Unstructured data analysis
  • Forensic source identification problem
  • Health science
  • Forensic statistics
  • Renewable energy engineering
  • Precision agriculture

Publications

  1. Clustering large datasets by merging K-means solutions. Melnykov, V. and Michael, S. (2020). Journal of Classification, 37(1), 97-123.
  2. Prioritizing climate‐smart agriculture: An organizational and temporal review. Gardezi, M., Michael, S., Stock, R., Vij, S., Ogunyiola, A. and Ishtiaque, A. (2022). Wiley Interdisciplinary Reviews: Climate Change, 13(2), e755.
  3. Spatial analysis of breast cancer mortality rates in a rural state. Schulz, M., Spors, E., Bates, K. and Michael, S. (2022). Preventing Chronic Disease, 19, E65.
  4. Mixture modeling of data with multiple partial right-censoring levels. Michael, S., Miljkovic, T. and Melnykov, V. (2020). Advances in Data Analysis and Classification, 14, 355-378.
  5. Solar irradiance forecasting in remote microgrids using Markov switching model. Shakya, A., Michael, S., Saunders, C., Armstrong, D., Pandey, P., Chalise, S. and Tonkoski, R. (2016). IEEE Transactions on Sustainable Energy, 8(3), 895-905

Department(s)

Related Links

Michael Research SiteGoogle ScholarORCID