Artificial intelligence · Optimization · Data science
Michael Hahsler
Clinical Associate Professor of Computer Science at Southern Methodist University
I develop machine-learning, statistical, and optimization methods for artificial intelligence and data science. My work emphasizes reproducible research through open-source software, including more than 15 widely used R packages. I also serve as an associate editor of the Journal of Statistical Software.
Profiles: Google Scholar · ORCID · GitHub · LinkedIn · R-universe · ResearchGate · StackOverflow
Open-source software
I am lead developer and maintainer of several widely used open-source R and Python tools that turn research in data mining, machine learning, and artificial intelligence into practical software.
Research and students
My research spans machine learning, combinatorial optimization, healthcare analytics, and bioinformatics. Read more about my research, published work, and students.
Contact
Department of Computer Science
Lyle School of Engineering, SMU
Caruth Hall, Room 431
Office hours: MWF 10-11AM + W 2-3PM
mhahsler (at) smu.edu
Flagship software projects
Association rule mining
arules
Infrastructure and fast algorithms for mining, representing, and analyzing frequent itemsets and association rules in R.
Density-based clustering
dbscan
Fast implementations of DBSCAN, HDBSCAN, OPTICS, LOF, and nearest-neighbor search for spatial data analysis in R.
Decision making under uncertainty
pomdp
Tools for defining, solving, simulating, and analyzing partially observable Markov decision process models in R.
News
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gym-classics2 released for teaching reinforcement learning
gym-classics2 combines classic finite Markov decision processes, readable textbook algorithms, and visualization tools in a modern Gymnasium package.
Research and Innovation Week: Cameron Tofani Presented Her Work on Missing Protein Prediction in Biological Pathways
Cameron Tofani presented her transformer-based research on predicting missing proteins in biological pathways at SMU Research and Innovation Week.
New Reinforcement Learning Course
A new Spring 2026 course introduces the reinforcement-learning methods modern AI agents use to learn through environmental interaction.
Zerui Ma presented his work on Academic Recommender Systems at the 2025 AAAI Spring Symposium
Zerui Ma presented a goal-based recommender-system architecture for university curriculum advising at the 2025 AAAI Spring Symposium.
Common Curriculum Reading of Artificial Unitelligence Kick-off Lecture
Michael Hahsler delivered the kickoff lecture for the Spring 2025 Common Curriculum reading of Artificial Unitelligence.
The R Companion for Introduction to Data Mining is now a downloadable book
The R Companion for Introduction to Data Mining is now available as both a downloadable PDF and an interactive online book.