Martin Trapp
Assistant Professor In Machine Learning at WASP – Wallenberg AI, Autonomous Systems and Software Program
Helsinki, Finland
Summary
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Rockstar🎓
Top SchoolMartin Trapp is an assistant professor in machine learning with 11 years of experience bridging probabilistic research and production-grade code. Based in Helsinki, he combines postdoctoral research at Aalto University with active development on flagship open-source probabilistic programming tools like Turing.jl, where he has driven core refactors and bug fixes that improved numerical stability and Julia 1.0 compatibility. His background spans a PhD from TU Graz and engineering degrees from Vienna, reflecting a strong foundation in both theory and applied systems. Martin is skilled at transforming research ideas into robust implementations, particularly in Bayesian inference and probabilistic modeling. Colleagues describe him as a careful debugger and optimizer who focuses on core systems that quietly make complex models reliable.
11 years of coding experience
15 years of employment as a software developer
Vienna University of TechnologyDoktor (Ph.D.), Machine Learning, Doktor (Ph.D.), Machine Learning at Technische Universität GrazBachelor of Science (B.Sc.), Informatik, Bachelor of Science (B.Sc.), Informatik at FH Technikum Wien
Github Skills (9)
bayesian-statistics10
mcmc10
bayesian10
probabilistic-programming10
bayesian-inference10
julia10
mc9
machine-learning8
artificial-intelligence7
Programming languages (12)
Github contributions (5)
TuringLang/Turing.jl
Jun 2018 - Dec 2021
Bayesian inference with probabilistic programming.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 reviews, 182 commits, 38 PRs in 3 years 6 months
Contributions summary:Martin primarily focused on debugging and refactoring the `turing.jl` repository, specifically addressing a bug in `resampleSystematic` related to incorrect normalization and refactoring code for compatibility with Julia 1.0. The user also worked on transforming and optimizing the model's core features, which included code modifications in core functions and improvements in existing code. This involved a series of WIP commits, reflecting incremental progress on various functionalities within the project's internal workings.
bayesian-inferenceprobabilistic-programmingmachine-learningjulia-languageartificial-intelligence
trappmartin/trappmartin.github.io
Nov 2016 - Dec 2022
Personal page
Contributions:2 PRs, 94 pushes, 1 branch in 6 years 2 months
javascriptreactpersonal-page
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