About
Yarin Gal is an Associate Professor of Machine Learning at the University of Oxford's Department of Computer Science and a Tutorial Fellow at Christ Church College. He is also a Turing AI Fellow at the Alan Turing Institute and Director of Research at the UK Government’s AI Safety Institute. He leads the Oxford Applied and Theoretical Machine Learning (OATML) Research Group, focusing on Bayesian deep learning, AI safety, and uncertainty quantification.
Education
- PhD in Uncertainty in Deep Learning (2016)
Research Interests
His research integrates Bayesian methods with deep learning to address challenges in AI safety, uncertainty quantification, and robustness. Key areas include:
- Bayesian neural networks and approximate inference
- Uncertainty estimation in deep learning
- AI safety and interpretability
- Applications in autonomous driving, medical imaging, and NLP
Publications & Trends
His recent work spans Bayesian optimization, adversarial robustness, and continual learning. Notable contributions include Targeted Dropout for model pruning, Uncertainty in Autonomous Driving, and theoretical studies on adversarial examples in Bayesian networks.
Awards & Honors
- Turing AI Fellow
Teaching & Supervision
He has taught Advanced Machine Learning, Uncertainty in Deep Learning, and contributed to NASA's Frontier Development Lab. His current students include Kelsey Doerksen, Gunshi Gupta, and Shreshth Malik.
Labs & Teams
He leads the OATML Group, a multidisciplinary team advancing theoretical and applied machine learning, with a focus on safety and interpretability in AI systems.
