Julius Von Kügelgen
Machine Learning Engineer at ETH Zürich
Zurich, Zurich, Switzerland
Summary
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Senior🎓
Top SchoolJulius Von Kügelgen is a Machine Learning Engineer and Branco Weiss Fellow at ETH Zürich with eight years of research experience at the intersection of causal inference and machine learning. He completed a PhD collaboration between the University of Cambridge and the Max Planck Institute for Intelligent Systems and now focuses on theory-driven methods that bridge causality and practical ML applications. Julius combines rigorous mathematical grounding with hands-on experimentation, publishing and developing models that clarify when causal approaches improve predictive and decision-making systems. Based in Zurich, he brings academic depth to engineering problems, translating theoretical insights into reproducible research and prototype implementations. An understated strength is his fluency across theory and code, enabling him to both prove properties of algorithms and ship working research software.
8 years of coding experience
Pass without corrections, Pass without corrections at University of Cambridge
German, English, Spanish, French, Latin, Italian
Github Skills (12)
arxiv9
xai8
probabilistic7
causality7
machine-learning6
latex4
graphical-models4
bayesian4
tikz4
factor4
pytorch4
drawing3
Programming languages (2)
Github contributions (5)
Juliusvk/SSLCauseEffect
Mar 2019 - Jun 2020
Contributions:38 commits, 3 pushes in 1 year 3 months
Juliusvk/Semi-Generative-Modelling
Feb 2019 - Jun 2020
Contributions:3 pushes, 1 branch in 1 year 4 months
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