Ole-christoffer Granmo
Chair Of The Technical Steering Committee at Anzyz Technologies AS
Grimstad, Agder, Norway
Summary
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Senior🎓
Top SchoolOle-Christoffer Granmo is a professor and founding director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, combining deep academic leadership with entrepreneurial drive as co-founder of Anzyz Technologies and two healthcare and AI startups. He created the Tsetlin machine in 2018—a symbolic-learning approach that earned him AI research paper of the decade recognition from NORA—and has authored 180+ refereed papers across learning automata, bandits, Bayesian reasoning, reinforcement learning and computational linguistics. With a Dr. scient. in computer science from the University of Oslo, he has supervised dozens of masters and nine PhD students while coordinating multiple large research projects. He also serves as Chair of the Technical Steering Committee at Literal Labs, bringing governance and technical strategy to industry-facing AI efforts. Known for bridging theoretical innovation and practical impact, he began programming at age ten and has maintained a steady pattern of founding, advising, and scaling research-driven companies. Beyond publications, his career blends consulting, emergency management leadership and applied analytics, reflecting an uncommon mix of academic rigor and real-world problem solving.
8 years of coding experience
11 years of employment as a software developer
University of Oslo
Github Skills (48)
pattern-recognition10
sparse10
evaluation10
classification10
graph10
machine-learning10
sampling10
indexing10
weighted10
classifier10
game-theory10
autoencoder10
interpretable-machine-learning10
datasets10
regression10
Programming languages (5)
Github contributions (5)
cair/tmu
Nov 2021 - Feb 2023
Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
Contributions:7 releases, 705 commits, 8 PRs in 1 year 2 months
pythonpropositional-logicclassifiersparseconvolution
cair/convolutional-tsetlin-machine-tutorial
May 2019 - Jan 2021
Tutorial on the Convolutional Tsetlin Machine
Contributions:362 commits, 1 PR, 361 pushes in 1 year 8 months
deep-learningconvolutionaltsetlin-machine
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