Antonio Vergari is a Reader (Associate Professor) in Machine Learning and a member of the ELLIS Unit at the University of Edinburgh. His research focuses on efficient and reliable machine learning in the wild, tractable probabilistic modeling and neuro-symbolic AI by combining learning with complex reasoning. He is interested in unifying probabilistic reasoning. Recently, he has been awarded an ERC Starting Grant called “UNREAL - a Unified REAsoning Layer for Trustworthy ML”. Previously he has been a postdoc in the StarAI Lab lead by Guy Van den Broeck at UCLA. And before that he did a postdoc at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany supervised by Isabel Valera. He obtained a PhD in Computer Science and Mathematics at the University of Bari, Italy. He published several conference and journal papers in top-tier AI and ML venues such as NeurIPS, ICML, UAI, ICLR, AAAI, ECML-PKDD and more, several of which have been awarded oral and spotlight presentations and a Best Student Paper Runner-up award at UAI 2025. He frequently engages with the tractable probabilistic modeling and the deep generative models communities by organizing a series of events: the Tractable Probabilistic Modeling Workshop (ICML2019, UAI2021, UAI2022, UAI2023, UAI2025), the Tractable PRobabilistic Inference MEeting (T-PRIME) at NeurIPS 2019, CoLoRAI at AAAI2025 and ICML 2026 and presented a series of tutorials on complex probabilistic reasoning and models at UAI 2019, AAAI 2020, ECAI 2020, IJCAI 2021, NeurIPS 2022 and 2025 and AAAI 2025. He organized two Dagstuhl Seminars on these topics.