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.
11 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 (10)
deep-learning8
machine-learning8
python8
tensorflow7
neural-network6
ml5
deep-neural-networks5
pytorch3
numpy2
gaussian-processes2
Programming languages (3)
Github contributions (5)
cambridge-mlg/RAT-SPN
Jun 2019 - Jun 2020
Code for UAI'19: Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
Contributions:5 commits, 11 pushes, 1 branch in 11 months
deep-learning
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
numpypytorch
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