2026 Yarin Gal: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	46	6633	6441	397	392	164	24201

Overview

Yarin Gal is affiliated with the University of Oxford in the United Kingdom and has a significant body of research in the field of Computer Science. Their work primarily focuses on Artificial Intelligence, contributing extensively to subfields such as Computer Vision and Pattern Recognition, Molecular Biology, Global and Planetary Change, and Infectious Diseases.

Their research interest spans several topics, including:

Machine Learning and Algorithms
Machine Learning and Data Classification
Adversarial Robustness in Machine Learning
Topic Modeling
Domain Adaptation and Few-Shot Learning
Gaussian Processes and Bayesian Inference
Explainable Artificial Intelligence (XAI)

Yarin Gal has published a considerable number of papers, many of which have appeared in high-impact venues. Frequent publication venues include:

arXiv (Cornell University) with 126 publications
bioRxiv (Cold Spring Harbor Laboratory) with 10 publications
Nature with 7 publications
Zenodo (CERN European Organization for Nuclear Research) with 3 publications
Nature Communications with 2 publications

Some of the recent papers from their work include:

Inferring the effectiveness of government interventions against COVID-19, 2020, Science
Disease variant prediction with deep generative models of evolutionary data, 2021, Nature
AI models collapse when trained on recursively generated data, 2024, Nature
Detecting hallucinations in large language models using semantic entropy, 2024, Nature
Uncertainty Estimation Using a Single Deep Deterministic Neural Network, 2020, arXiv (Cornell University)

Frequent collaborators in Yarin Gal's research include:

Sören Mindermann
Pascal Notin
Andrew Jesson
Andreas Kirsch
Jan Brauner

Best Publications

Dropout as a Bayesian approximation: representing model uncertainty in deep learning

Yarin Gal;Zoubin Ghahramani

6822
Citations
What uncertainties do we need in Bayesian deep learning for computer vision

Alex Kendall;Yarin Gal

3124
Citations
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Roberto Cipolla;Yarin Gal;Alex Kendall

2752
Citations
A theoretically grounded application of dropout in recurrent neural networks

Yarin Gal;Zoubin Ghahramani

1684
Citations
Deep Bayesian active learning with image data

Yarin Gal;Riashat Islam;Zoubin Ghahramani

1019
Citations
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference

Yarin Gal;Zoubin Ghahramani

874
Citations
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Alex Kendall;Yarin Gal;Roberto Cipolla

499
Citations
Real Time Image Saliency for Black Box Classifiers

Piotr Dabkowski;Yarin Gal

467
Citations
Concrete Dropout

Yarin Gal;Jiri Hron;Alex Kendall

339
Citations
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

Andreas Kirsch;Joost van Amersfoort;Yarin Gal

312
Citations
Uncertainty Estimation Using a Single Deep Deterministic Neural Network

Joost van Amersfoort;Lewis Smith;Yee Whye Teh;Yarin Gal

290
Citations
Towards Robust Evaluations of Continual Learning

Sebastian Farquhar;Yarin Gal

249
Citations
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning

Rowan McAllister;Yarin Gal;Alex Kendall;Mark van der Wilk

246
Citations
Understanding Measures of Uncertainty for Adversarial Example Detection

Lewis Smith;Yarin Gal

224
Citations
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval

Unknown

211
Citations
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

Jishnu Mukhoti;Yarin Gal

199
Citations
Towards global flood mapping onboard low cost satellites with machine learning.

Gonzalo Mateo-Garcia;Joshua Veitch-Michaelis;Lewis Smith;Silviu Vlad Oprea

185
Citations
Dropout inference in Bayesian neural networks with alpha-divergences

Yingzhen Li;Yarin Gal

175
Citations
Learning Invariant Representations for Reinforcement Learning without Reconstruction

Amy Zhang;Rowan Thomas McAllister;Roberto Calandra;Yarin Gal

167
Citations
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Mohammad Emtiyaz Khan;Didrik Nielsen;Voot Tangkaratt;Wu Lin

163
Citations
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

Yarin Gal;Mark van der Wilk;Carl Rasmussen

154
Citations
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Luisa Zintgraf;Kyriacos Shiarlis;Maximilian Igl;Sebastian Schulze

104
Citations
Can autonomous vehicles identify, recover from, and adapt to distribution shifts?

Angelos Filos;Panagiotis Tigkas;Rowan McAllister;Nicholas Rhinehart

50
Citations

Frequent Co-Authors

Zoubin Ghahramani University of Cambridge

Yee Whye Teh University of Oxford

Sergey Levine University of California, Berkeley

Samir Bhatt Imperial College London

Marta Kwiatkowska University of Oxford

Joni Dambre Ghent University

Arthur Gretton University College London

Roberto Cipolla University of Cambridge

Guy Schumann University of Bristol

Debora S. Marks Harvard University

External Links

Personal website of Yarin Gal Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens doors to a wide range of online education options and career opportunities. Many online colleges that accept 2.0 gpa make technology degrees more accessible to a broader group of students, helping those with diverse academic backgrounds start their journey in IT.

If you want to accelerate your studies, consider enrolling in an accelerated computer science degree. These fast-track programs allow motivated learners to complete their education and enter the workforce sooner.

Expanding your career options can also mean looking at related disciplines. For example, with a strong computer science foundation, you can pursue fields like environmental science or engineering. Explore what doors open by seeing what can you do with an environmental science degree or researching environmental engineering degrees online.

By considering these online pathways, you can find programs tailored to your academic record, time frame, and career interests, helping you shape a technology-driven future.