Thijs Van De Laar
Assistant Professor at Eindhoven University of Technology
Nijmegen, Gelderland, Netherlands
Summary
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Senior🎓
Top SchoolThijs Van De Laar is an assistant professor and researcher with 14 years of experience developing real-time adaptive intelligent systems that bridge neuroscience, physics, and probabilistic programming. His work focuses on scaling Active Inference for industrial use, creating theory and software tools that enable agents to make situated decisions under uncertainty. At Eindhoven University of Technology he progressed from PhD research in machine learning for audio to postdoctoral and faculty roles in Bayesian signal processing and automated algorithm design. He combines academic rigor with practical engineering, having co-founded data- and community-oriented startups and built accessible tools for social initiatives. Based in Nijmegen, he brings a rare mix of theoretical depth and hands-on implementation aimed at real-time, deployable intelligent agents.
14 years of coding experience
13 years of employment as a software developer
MSc., Natural Science, MSc., Natural Science at Radboud UniversityDoctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Eindhoven University of TechnologyArtificial Intelligence, Artificial Intelligence at Vrije Universiteit Amsterdam (VU Amsterdam)VWO, N&T + N&G, VWO, N&T + N&G at Stedelijk College Eindhoven
Stackoverflow
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Github Skills (24)
algorithms10
variational-inference10
probabilistic-programming10
machine-learning10
inference-engine10
message-passing10
probabilistic-graphical-models10
bayesian-methods10
bayesian-inference10
julia10
hugo9
graph9
observable8
inference8
asynchronous8
Programming languages (4)
Github contributions (5)
biaslab/ForneyLab.jl
Apr 2014 - Oct 2022
Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.
Contributions:3 releases, 21 reviews, 1149 commits in 8 years 7 months
bayesian-inferencejuliamessage-passingbayesian-methodsmachine-learning
biaslab/LAIF
Aug 2023 - Oct 2024
Experiments with deriving epistemics aware message passing algos
Contributions:2 releases, 3 PRs, 4 pushes in 1 year 1 month
message-passing
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