Yarin Gal
Yarin Gal studies uncertainty in deep learning. His work connects practical neural-network methods to Bayesian reasoning and model reliability. Gal and Zoubin Ghahramani coauthored the original dropout study. It connects dropout in neural networks to approximate Bayesian inference and uses that interpretation to estimate model uncertainty. Gal started Oxford’s Applied and Theoretical Machine Learning group in October 2018. The group covers both mathematical and applied machine-learning questions. He is an Oxford machine-learning professor and formerly served as Frontier AI Taskforce research director, where he established its Safeguards team. His research includes Bayesian inference, generative models and AI safety.
- Now at
- University of Oxford
- Works
- 295
Publication numbers from OpenAlex, as of 14 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.
Timeline
-
2015
Dropout as Bayesian approximation
Gal and Zoubin Ghahramani coauthored the original dropout study. It connects dropout in neural networks to approximate Bayesian inference and uses that interpretation to estimate model uncertainty.
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2018
Oxford machine-learning group
Gal started Oxford’s Applied and Theoretical Machine Learning group in October 2018. The group covers both mathematical and applied machine-learning questions.
-
As of October 2026
Research and AI safeguards
He is an Oxford machine-learning professor and formerly served as Frontier AI Taskforce research director, where he established its Safeguards team. His research includes Bayesian inference, generative models and AI safety.
Sources and credits
Photo: ellis.eu; Yarin Gal profile photograph; creator not stated. License: Not stated by source. Source: ellis.eu. Cropped to a square and resized. Ask us to remove or replace this photo.