Rasmus Bonnevie
ML & Data Tech Lead
Copenhagen, Capital Region of Denmark
Summary
🤩
Rockstar🎓
Top SchoolRasmus Bonnevie is an ML & Data Tech Lead based in Copenhagen with a decade of experience building decision support systems for healthcare using Bayesian probabilistic models and knowledge engineering. He holds an Honours M.Sc. in Computational and Applied Mathematics and spent nearly three years as a PhD student at DTU focusing on approximate inference for Bayesian learning, complemented by a visiting research stint at Oxford working on Gaussian processes and variational methods. At Unumed he progressed from Research Scientist to Tech Lead, translating probabilistic research into production-ready clinical decision support. Technically he specializes in advanced probabilistic machine learning and Bayesian statistics while maintaining broad fluency across modern ML paradigms, including deep learning. An active open-source contributor, he improved GPflow’s parameter randomization and testing to enhance flexibility and backwards compatibility in a widely used Gaussian process library. Colleagues would note his rare combination of rigorous probabilistic theory, practical engineering, and domain-driven design for healthcare applications.
10 years of coding experience
Nanyang Technological UniversityBachelor of Engineering (B.E.), Computational and Applied Mathematics, Bachelor of Engineering (B.E.), Computational and Applied Mathematics at Danmarks Tekniske Universitet / Technical University of DenmarkMaster of Science (M.Sc.), Computational and Applied Mathematics, Honours, Master of Science (M.Sc.), Computational and Applied Mathematics, Honours at Danmarks Tekniske Universitet
Danish, English, french (out of practice), spanish (out of practice)
Github Skills (12)
bayesian-statistics10
machine-learning10
bayesian10
tensorflow10
python10
gpflow10
gaussian-processes10
numpy9
testing9
unit-test9
unit-testing9
variational-inference8
Programming languages (8)
Github contributions (5)
GPflow/GPflow
Jan 2017 - Aug 2017
Gaussian processes in TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 9 PRs, 14 pushes in 6 months
Contributions summary:Rasmus primarily contributed to adding and testing features related to parameter randomization within the GPflow library. This involved implementing methods to sample random values from priors, including support for various distributions like Gaussian, LogNormal, and Beta. Their work extended to handling multi-dimensional parameters and overriding default randomization behavior, improving the flexibility and functionality of the library. The user also addressed backwards compatibility issues in the testing framework.
information-theorygpflowdeep-learningmachine-learningmarkov-chain-monte-carlo
Bonnevie/rebar
Oct 2017 - Jun 2018
Contributions:30 commits, 37 pushes, 2 branches in 8 months
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