Michael Lash

Michael Lash
  • Assistant Professor
  • Analytics, Information and Operations Management academic area

Contact Info

785-864-0796
Capitol Federal Hall, Room 4166
Lawrence

Personal Links

Education —

Ph.D. in Computer Science, University of Iowa, 2018
M.S. in Computer Science, University of Iowa, 2017
B.A. in Geoinformatics, University of Iowa, 2014

Research —

Dr. Michael T. Lash's research focuses broadly on machine learning, data mining, and business analytics.

Research interests:

  • Data Mining
  • Machine Learning
  • Decision Making with Machine Learning
  • Reinforcement Learning
  • Recommendation
  • Outcome Optimization
  • Graph Learning
  • Causal Learning
  • Predictive and Prescriptive Analytics

Teaching —

Dr. Michael T. Lash is broadly interested in teaching analytics to students at all levels. Lash is particularly interested teaching machine learning methods that align with the current business landscape, including text and graph learning/mining methodologies, among others.

Teaching interests:

  • Business Analytics
  • Machine Learning
  • Database Management

Selected Publications —

Lash, M. T., Slater, J., Polgreen, P. M., & Segre, A. M. (2019). 21 Million Opportunities: a 19 Facility Investigation of Factors Affecting Hand-Hygiene Compliance via Linear Predictive Models [Journal Articles]. Journal of Healthcare Informatics Research, 3(4), 393–413. https://doi.org/10.1007/s41666-019-00048-1
Lash, M. T., Zhang, M., Zhou, X., Street, W. N., & Lynch, C. F. (2018). Deriving enhanced geographical representations via similarity-based spectral analysis: predicting colorectal cancer survival curves in Iowa [Journal Articles]. International Journal of Data Mining and Bioinformatics, 21(3), 183. https://doi.org/10.1504/ijdmb.2018.097677
Lash, M. T., Lin, Q., Street, W. N., & Robinson, J. G. (2017). A Budget-Constrained Inverse Classification Framework for Smooth Classifiers [Conference Proceedings]. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE. https://doi.org/10.1109/icdmw.2017.174
Lash, M. T., Sun, Y., Zhou, X., Lynch, C. F., & Street, W. N. (2017). Learning rich geographical representations: Predicting colorectal cancer survival in the state of Iowa [Conference Proceedings]. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. https://doi.org/10.1109/bibm.2017.8217754
Lash, M. T., Slater, J., Polgreen, P. M., & Segre, A. M. (2017). A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models [Conference Proceedings]. In 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE. https://doi.org/10.1109/ichi.2017.12
Lash, M. T., Lin, Q., Street, W. N., Robinson, J. G., & Ohlmann, J. (2017). Generalized Inverse Classification [Conference Proceedings]. In Proceedings of the 2017 SIAM International Conference on Data Mining (pp. 162–170). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611974973.19
Gerke, A. K., Tang, F., Lash, M. T., Schappet, J., Phillips, E., & Polgreen, P. M. (2017). A web-based registry for patients with sarcoidosis [Journal Articles]. Sarcoidosis Vasculitis and Diffuse Lung Diseases (SVDLD), 34(1), 26–34. https://doi.org/10.36141/svdld.v34i1.5129
Lash, M. T., & Zhao, K. (2016). Early Predictions of Movie Success: The Who, What, and When of Profitability [Journal Articles]. Journal of Management Information Systems, 33(3), 874–903. https://doi.org/10.1080/07421222.2016.1243969
Lash, M. T., Fu, S., Wang, S., & Zhao, K. (2015). Early Predictions of Movie Success: The Who, What, and When [Conference Proceedings]. In Proceedings of the 2015 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP) (pp. 345–349). Springer. https://doi.org/10.1007/978-3-319-16268-3_41
Gupta, A., Lash, M. T., & Nachimuthu, S. K. (accepted/in press). Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence [Journal Articles]. Expert Systems with Applications.
Lash, M. T., & Street, W. N. (accepted/in press). Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification [Conference Proceedings]. In Bioinformatics and Biomedicine Workshops (BIBMW), IEEE International Conference on.