Antonio Vergari
Reader (Associate Professor) In Machine Learning At The University Of Edinburgh
City of Edinburgh, Scotland, United Kingdom
Summary
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Senior🎓
Top SchoolAntonio Vergari is a Reader (Associate Professor) in Machine Learning at the University of Edinburgh with 15 years of experience building reliable probabilistic ML systems that blend rich symbolic reasoning with efficient deep learning inference. As PI of the APRIL lab he focuses on tractable probabilistic models and provable reliability in real-world settings, pushing NeSy (neuro-symbolic) approaches toward practical deployment. His background spans top research institutions (UCLA, Max Planck) and applied roles from clinical data management to generative design, reflecting a rare mix of theoretical depth and practical engineering. A top-graduated computer scientist from Università di Bari, he brings an interdisciplinary perspective and a taste for simplicity—selfie non multiplicanda praeter necessitatem—when designing scalable, interpretable ML.
15 years of coding experience
7 years of employment as a software developer
Master's degree, Computer Science, 110/110 cum laude and special commendation by the commission, Master's degree, Computer Science, 110/110 cum laude and special commendation by the commission at Università degli Studi di Bari
Github Skills (107)
probabilistic-programming10
encoding10
mixture-model10
sampling10
autoencoder10
sum10
probability10
distributions10
binding10
information-theory10
bayesian10
probabilistic-models10
sum-product10
probabilistic10
probabilistic-graphical-models10
Programming languages (7)
Github contributions (5)
arranger1044/probabilistic-circuits
Apr 2020 - May 2022
A curated collection of papers on probabilistic circuits, computational graphs encoding tractable probability distributions.
Contributions:1 review, 54 commits, 15 PRs in 2 years 1 month
distributionscircuitsprobability-distributionsmachine-learningencoding
arranger1044/iSoul
Oct 2010 - May 2012
Contributions:56 commits, 4 comments in 1 year 7 months
mac-ossoulseekmacos-xmacos
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