Antonio Vergari
About
Dr. Antonio Vergari is a Reader (Associate Professor equivalent) in the School of Informatics at the University of Edinburgh, where he is a leading member of the Institute for Adaptive and Neural Computation (ANC). A specialist in the mathematical foundations of trustworthy AI, Dr. Vergari’s research focuses on Probabilistic Machine Learning and the development of Probabilistic Circuits (PCs). His work (2025–2026) aims to bridge the gap between "black-box" deep learning and transparent, tractable reasoning by creating Neuro-Symbolic AI systems that are both highly accurate and verifiably reliable. By engineering models that can perform exact and efficient probabilistic inference—such as computing marginals or conditionals in linear time—he enables the deployment of AI in high-stakes domains like healthcare and autonomous systems where understanding "why" a model makes a prediction is as critical as the prediction itself.
Research Performance Summary
First Recorded Paper
A Co-Clustering approach for Sum-Product Network Structure Learning
Year: 2014
Citations: 0
Venue: N/A
Latest Recorded Paper
How to Square Tensor Networks and Circuits Without Squaring Them
Year: 2026
Citations: 3
Venue: The Fourteenth International Conference on Learning Representations
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.