Michael Lash

Michael Lash
  • Assistant Professor
  • Analytics, Information, Operations 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 —

Gupta, A., Lash, M., Nachimuthu, S. (2021). Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence. Expert Systems with Applications - Volume 169. https://doi.org/10.1016/J.ESWA.2020.114476.
Lash, M., Slater, J., Polgreen, P., Segre, A. (2019). 21 Million Opportunities: a 19 Facility Investigation of Factors Affecting Hand-Hygiene Compliance via Linear Predictive Models. Journal of Healthcare Informatics Research - Issue 4 | Volume 3. https://doi.org/10.1007/S41666-019-00048-1.
Street, N., Lynch, C., Zhou, X., Zhang, M., Lash, M., Street, W. (2018). Deriving enhanced geographical representations via similarity-based spectral analysis: predicting colorectal cancer survival curves in Iowa. International Journal of Data Mining and Bioinformatics - Issue 3 | Volume 21. https://doi.org/10.1504/IJDMB.2018.10018956.
Lash, M., Zhang, M., Zhou, X., Street, N., Lynch, C. (2018). Deriving enhanced geographical representations via similarity-based spectral analysis: predicting colorectal cancer survival curves in Iowa. International Journal of Data Mining and Bioinformatics - Issue 3 | Volume 21. https://doi.org/10.1504/IJDMB.2018.097677.
Lash, M., Sun, Y., Zhou, X., Lynch, C., Street, N. (2017). Learning rich geographical representations: Predicting colorectal cancer survival in the state of Iowa. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Pages 778-785. https://doi.org/10.1109/BIBM.2017.8217754.
Lash, M., Lin, Q., Street, N., Robinson, J. (2017). A Budget-Constrained Inverse Classification Framework for Smooth Classifiers. IEEE International Conference on Data Mining Workshops (ICDMW) - Pages 1184-1193. https://doi.org/10.1109/ICDMW.2017.174.
Lash, M., Slater, J., Polgreen, P., Segre, A. (2017). A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models. IEEE International Conference on Healthcare Informatics (ICHI) - Pages 66-73. https://doi.org/10.1109/ICHI.2017.12.
Lash, M., Lin, Q., Street, W., Robinson, J., Ohlmann, J. (2017). Generalized Inverse Classification. Proceedings of the 2017 SIAM International Conference on Data Mining - Pages 162-170. https://doi.org/10.1137/1.9781611974973.19.
Gerke, A., Tang, F., Lash, M., Schappet, J., Phillips, E., Polgreen, P. (2017). A web-based registry for patients with sarcoidosis. Sarcoidosis vasculitis and diffuse lung diseases (SVDLD) - Issue 1 | Volume 34. https://doi.org/10.36141/svdld.v34i1.5129.
Lash, M., Zhao, K. (2016). Early Predictions of Movie Success: The Who, What, and When of Profitability. Journal of Management Information Systems - Issue 3 | Volume 33. https://doi.org/10.1080/07421222.2016.1243969.
Lash, M., Fu, S., Wang, S., Zhao, K. (2015). Early Prediction of Movie Success — What, Who, and When. Social Computing, Behavioral-Cultural Modeling, and Prediction [Conference] - Volume 9021 | Pages 345-349. https://doi.org/10.1007/978-3-319-16268-3_41.
Gupta, A., Lash, M., Nachimuthu, S. Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence. Expert Systems with Applications.
Lash, M., Street, W. Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification. Bioinformatics and Biomedicine Workshops (BIBMW), IEEE International Conference on.