Robert Peharz
Associate Professor
Graz, Styria, Austria
Summary
👤
Senior🎓
Top SchoolRobert Peharz is an Associate Professor and machine learning researcher with a decade of experience specializing in probabilistic ML, tractable inference, causality, and physics-informed and neurosymbolic learning. He has held academic posts across Europe, including Marie-Curie fellowship at Cambridge, assistant professorships at TU Eindhoven and TU Graz, and a postdoc at Medical University Graz, blending theoretical advances with applied experimental design and Bayesian optimization. His work focuses on making inference both expressive and tractable, and he actively explores active learning and experimental design to close the loop between data acquisition and model uncertainty. Based in Graz, Austria, he combines deep probabilistic expertise with a background in telematics and a knack for translating interdisciplinary problems—especially at the intersection of physics and ML—into practical, scalable methods.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Technische Universität Graz
Github Skills (17)
python9
machine-learning9
deep-learning9
tensorflow8
data-science7
projection7
neural-network7
dimensions7
autoencoder6
deep-neural-networks5
unsupervised-learning5
ml5
simplex5
gaussian3
pytorch3
Programming languages (3)
Github contributions (5)
cambridge-mlg/RAT-SPN
Jun 2019 - Jun 2020
Contributions:5 commits, 11 pushes, 1 branch in 11 months
smatmo/ProjectionOntoSimplex
Aug 2019 - Apr 2024
Simplex projection (numpy, pytorch), from Duchi et al., "Efficient Projections onto the l1-Ball for Learning in High Dimensions", ICML 2008
Contributions:3 pushes, 1 branch in 4 years 8 months
pytorchautoencoderprojectionsimplexunsupervised-learning
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