1. Academic Summary
Philipp Hennig is a German machine learning researcher and Professor for the Methods of Machine Learning at the University of Tübingen. He specializes in probabilistic machine learning, inference and computation, spanning foundational research and efficient learning systems. He played a key role in establishing probabilistic numerics as a field and authored the textbook Probabilistic Numerics — Computation as Machine Learning (Cambridge University Press, 2022).
Since July 2025, Hennig co-directs the Tübingen AI Center and serves on steering committees for major research clusters in machine learning.
He is a founding Fellow and Co-Director of the ELLIS Program on Theory and Algorithms of Modern Learning Systems.
2. Education
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Ph.D. in Machine Learning (Inference) — University of Cambridge, UK (in the group of Sir David J.C. MacKay)
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Studied Physics at the University of Heidelberg, Germany and Imperial College London, UK.
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3. Professional Experience
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Professor for Methods of Machine Learning, University of Tübingen (since 2018)
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Director, Tübingen AI Center (with Matthias Bethge) (2025–present)
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Founding ELLIS Fellow & Co-Director, Theory, Algorithms & Computations of Modern Learning Systems
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Member of Steering Committees for the Machine Learning in Science Excellence Cluster and other research initiatives
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4. Research Interests
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Probabilistic Machine Learning
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Probabilistic Numerics (Computation as ML)
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Bayesian Inference & Approximate Computation
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Algorithms for Reliable and Efficient Learning
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Foundations of Learning Systems and Uncertainty Quantification
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5. Key Contributions
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Co-founded the field of probabilistic numerics, which interprets numerical computation through probabilistic inference frameworks.
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Authored a textbook synthesizing probabilistic and computational perspectives on ML (Probabilistic Numerics — Computation as Machine Learning).
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Leads research and teaching efforts in probabilistic ML theory and applications at University of Tübingen.
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Held key roles in shaping European AI research networks (ELLIS) and contributed to foundational machine learning clusters.
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