DrBen Moseley
Assistant Professor in AI (Schmidt AI in Science Fellow)
Department of Earth Science & Engineering - Faculty of Engineering
Orcid identifier0000-0003-2238-1783 (opens in a new tab)
- Assistant Professor in AI (Schmidt AI in Science Fellow)Department of Earth Science & Engineering - Faculty of Engineering
RESEARCH
I lead the Scalable Scientific Machine Learning Lab, which is part of the Department of Earth Science and Engineering at Imperial College London.
We accelerate scientific research by designing scientific machine learning (SciML) algorithms and applying them to impactful problems across science.
We develop SciML techniques such as physics-informed neural networks, hybrid ML-numerical algorithms, and physics-based computer vision, and use them to accelerate simulations, better extract knowledge from data, discover new physical models, and improve experiment design. We focus on designing SciML algorithms which are 1) robust, designing physically-grounded workflows that generalise well, and 2) scalable, building algorithms that inherently handle multi-scale, high-dimensional, noisy, and realistic systems.
We have applied our SciML algorithms to many impactful problems, including to improve the accuracy of multi-scale simulation, de-noise low-light satellite images of the Moon’s surface, and extract new insights about quantum theories. We collaborate with many academic and industry partners to identify and solve impactful problems.
We are a highly cross-disciplinary team: our members are experts across machine learning, applied mathematics, high-performance computing, and in domain-specific areas including geophysics, climate science, and planetary science.
Learn more about us here: https://scalable-sciml-lab.org/
We accelerate scientific research by designing scientific machine learning (SciML) algorithms and applying them to impactful problems across science.
We develop SciML techniques such as physics-informed neural networks, hybrid ML-numerical algorithms, and physics-based computer vision, and use them to accelerate simulations, better extract knowledge from data, discover new physical models, and improve experiment design. We focus on designing SciML algorithms which are 1) robust, designing physically-grounded workflows that generalise well, and 2) scalable, building algorithms that inherently handle multi-scale, high-dimensional, noisy, and realistic systems.
We have applied our SciML algorithms to many impactful problems, including to improve the accuracy of multi-scale simulation, de-noise low-light satellite images of the Moon’s surface, and extract new insights about quantum theories. We collaborate with many academic and industry partners to identify and solve impactful problems.
We are a highly cross-disciplinary team: our members are experts across machine learning, applied mathematics, high-performance computing, and in domain-specific areas including geophysics, climate science, and planetary science.
Learn more about us here: https://scalable-sciml-lab.org/