The Oxford AI for Science lab is a part of the Department of Computer Science at the University of Oxford, and is led by Atılım Güneş Baydin.
The lab specializes in probabilistic machine learning and scientific discovery. We collaborate with experts in disciplines including particle physics, heliophysics, astrobiology, Earth science, and computational social science, making contributions to solve important problems in these domains through application and development of artificial intelligence (AI) methods. Highlights of our work include supercomputing-scale Bayesian inference in simulators of the Standard Model of particle physics, new calibration techniques for the NASA Solar Dynamics Observatory, and deploying machine learning algorithms onboard spacecraft launched to Earth orbit.
Our research has been funded by NASA, European Space Agency, US Department of Energy, and UK Space Agency.
Research Areas
Differentiable programming, probabilistic programming, simulation-based inference, Bayesian methods
Press Releases and Media Coverage
- May 2024: Our paper “Managing extreme AI risks amid rapid progress” is published in Science
- Oct 2023: Our work on managing AI risks is featured by the TIME Magazine and on the front page of The Guardian.
- Oct 2022: A framework for more robust, reliable, and responsible machine learning systems, MIT Media Lab
- Mar 2022: Simple mathematical trick could slash AI development time in half, New Scientist, issue 3378
- Jul 2021: Artificial Intelligence Helps Improve NASA’s Eyes on the Sun, NASA front page
- Jun 2021: Artificial Intelligence pioneered at Oxford to detect floods launches into space, University of Oxford front page
- Jan 2020: Solving NASA’s Toughest Challenges with AI; Sun: Forecasting Solar Flares, Google & Intel
- Nov 2019: Etalumis ‘reverses’ simulations to reveal new science, Phys.org
- Nov 2019: NASA Takes a Cue From Silicon Valley to Hatch Artificial Intelligence Technologies, NASA front page
- Nov 2019: Etalumis ‘Reverses’ Simulations to Reveal New Science: New Probabilistic Programming Framework a Finalist for Best Paper at SC19, Lawrence Berkeley Lab
Recent Work
Collaborators
