Artificial intelligence · Optimization · Data science

Turning complex data into useful information and decisions

I develop machine-learning and optimization methods for discovering structure in complex data and making decisions under uncertainty. My work connects methodological research with reproducible open-source software and applications in healthcare, bioinformatics, earth science, and engineering.

Diagram connecting Michael Hahsler's research methods, application areas, and supporting organizations — Research overview (PDF)

Research themes

Discovering structure

Pattern discovery and machine learning

Methods for association-rule and sequence mining, data-stream clustering, recommender systems, density-based clustering, and interpretable data visualization.

Association rule mining · Stream mining · Clustering · Recommender systems

Choosing actions

Decision-making and optimization

Models and algorithms for reinforcement learning, Markov and partially observable Markov decision processes, optimal ordering, scheduling, seriation, and routing.

POMDPs · Traveling salesperson problems · Seriation

Putting methods to work

Applied data science

Collaborative applications in healthcare analytics, bioinformatics, quantitative marketing, earth science, manufacturing, and engineering.

Healthcare · Bioinformatics · Earth science

Featured recent work

Healthcare analytics · 2025

Improving access to kidney care

Analytics and simulation-optimization models reveal barriers in emergent dialysis and study how transplant oversight affects waitlist management.

Artificial intelligence · 2025

Attributing AI-generated content

Direct-origin detection tests whether transformer-based language models can distinguish their own output from human-written text.

Decision-making · 2024

MDP, POMDP and RL

Software for defining, solving, simulating, and analyzing Markov decision processes and partially observable MDPsing planning and reinforcement learning.

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