Michael Hahsler, Ph.D.



Michael Hahsler, Ph.D.

Associate Professor, Computer Science

Office Location: Caruth Hall 431

Send Email



  • Ph.D. in Applied Computer Science, Wirtschafts universität Wien, Austria


Michael Hahsler is an Associate Professor of Computer Science at SMU Lyle. His research interests lie in the intersection of computer science, statistical methods, and combinatorial optimization with applications in artificial intelligence, machine learning, data mining, and data science.

He is the principal developer of several popular R data mining and machine learning extension packages. His research has been funded by NSF, NIH, and NIST.


Dr. Hahsler has published over 90 technical papers in peer-reviewed international journals and conference proceedings and has organized several workshops. He is a past secretary of the INFORMS Data Mining Section, and he currently serves as an associate editor of the Journal of Statistical Software and of the INFORMS Journal on Computing.


Honors and Awards

  • 2021-2022 HOPE Professor of the Year Award nominee
  • D Magazine’s D CEO Healthcare featured our diabetes research, 2020
  • IBM's 2014 The Great Mind Challenge - Watson Edition, winning team
  • Lyle Graduate Student Council Outstanding Faculty Award, Computer Science, 2011





  • Artificial Intelligence
  • Machine Learning
  • Data Mining
  • Data Science

Recent Publications 

  • Farzad Kamalzadeh, Vishal Ahuja, Michael Hahsler, and Michael E. Bowen. An analytics-driven approach for optimal individualized diabetes screening. Production and Operations Management, 30(9):3161-3191, September 2021.
  • Xinyi Ding, Zohreh Raziei, Eric C. Larson, Eli V. Olinick, Paul Krueger, and Michael Hahsler. Swapped face detection using deep learning and subjective assessment. EURASIP Journal on Information Security, 2020(6):1-12, May 2020.
  • Michael Hahsler, Matthew Piekenbrock, and Derek Doran. dbscan: Fast density-based clustering with R. Journal of Statistical Software, 91(1):1-30, 2019.
  • Michael Hahsler. An experimental comparison of seriation methods for one-mode two-way data. European Journal of Operational Research, 257:133-143, February 2017.
  • Michael Hahsler and Matthew Bolaños. Clustering data streams based on shared density between micro-clusters. IEEE Transactions on Knowledge and Data Engineering, 28(6):1449-1461, June 2016.


Personal website