Seth Axen
Machine Learning Research Engineer at Cluster of Excellence Machine Learning: New Perspectives for Science, University of Tübingen
Tübingen, Baden-Württemberg, Germany
Summary
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Rockstar🎓
Top SchoolSeth Axen is a Machine Learning Research Engineer with 11 years of experience applying mathematics, statistics, and software engineering to scientific problems from archaeology to medicine. Based at the ML ⇌ Science Colab in Tübingen, he leads workshops, provides hands-on consultations, and drives team projects that turn complex scientific data into actionable ML solutions while maintaining open-source tooling. A core contributor to ArviZ and an active developer on high-profile projects like Julia and Zygote, he brings deep numerical computing and automatic-differentiation expertise—implementing robust matrix algorithms and adjoints that improve performance and stability. He holds a PhD in Biomedical Informatics from UCSF and has a track record of translating research-grade methods into reusable software and reproducible analyses. Notably, his contributions span both Python and Julia ecosystems, bridging statistical Bayesian workflows with high-performance numerical libraries.
11 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy (Ph.D.), Biomedical Informatics, Doctor of Philosophy (Ph.D.), Biomedical Informatics at University of California, San FranciscoBS, Biochemistry, BS, Biochemistry at University of California, Los Angeles
Stackoverflow
71reputation
790reached
3answers
1question
Github Skills (43)
algorithm10
algorithms10
matrix10
python10
testing10
statistics10
machine-learning10
hpc10
math10
distributions10
data-structure10
gradient10
maths10
numpy10
math-functions10
Programming languages (16)
Github contributions (5)
arviz-devs/arviz
Nov 2019 - Nov 2022
Exploratory analysis of Bayesian models with Python
Role in this project:
Data Scientist
Data ScientistContributions:72 reviews, 12 commits, 23 PRs in 3 years
Contributions summary:Seth primarily contributed to the statistical and analytical capabilities of the ArviZ library, focusing on Bayesian model analysis. They refactored and improved existing functions such as `loo` and `psislw`, addressing issues related to posterior usage and weight smoothing. Furthermore, the user enhanced the library's plotting functionality and updated documentation and example data.
pythonexploratorybayesian-inferencemachine-learningbayesian-models
JuliaStats/Distributions.jl
Dec 2019 - Jan 2022
A Julia package for probability distributions and associated functions.
Role in this project:Data Scientist
Data ScientistContributions:130 reviews, 5 commits, 14 PRs in 2 years 1 month
Contributions summary:Seth's contributions center around enhancing the `distributions.jl` package, which focuses on probability distributions and related functions. Their commits introduce new functionality like the LKJCholesky distribution and the Censored distribution, expanding the package's capabilities. The user also refactors code to improve efficiency, such as replacing loops with sums, and optimizes existing features with fixes. Furthermore, they add comprehensive testing, including argument promotions, to ensure the quality and reliability of the implemented probability distributions.
julia-packagedistributionsstatisticsdata-scienceprobability-distributions
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