About

About me

I am currently a postdoctoral fellow at the ETH Zürich AI Center and member of the ETH Zürich Computational and Applied Mathematics Lab (2022-present). I am also a machine learning lead for NASA’s Frontier Development Lab (2019-present).

Prior to this I completed my PhD on physics and machine learning at the University of Oxford’s Center for Autonomous Intelligent Machines and Systems (2017-2022), I was a scientific machine learning intern at NVIDIA (2022), a geophysicist at BP (2012-2017), and studied physics (masters degree) at Durham University, UK (2008-2012).

GitHub
LinkedIn
Twitter
Google Scholar
Mail

Research interests

I am interested in machine learning for physics. More specifically, I want to understand how best we can combine physical principles and machine learning together in more nuanced ways to produce powerful and scalable tools for AI researchers and other scientists alike. I want to apply these tools to complex, real-world problems, changing the world for good. In my work I have developed new physics-informed machine learning algorithms for solving differential equations and applied state-of-the-art physics-based machine learning to many different real-world scientific problems, ranging from searching for water on the Moon to tracking elephants in Kenya.

I also want to inform the world about AI. I do this by writing AI blogs, teaching, releasing open-source code and publishing research on the intersection of AI and physics.

News

  • [Jan 23] I will be a lecturer for ETH Zürich’s Deep Learning in Scientific Computing course.
  • [Sept 22] I have joined ETH Zürich AI Center as a postdoctoral fellow.
  • [Aug 22] Our Geophysical Research Letters paper which characterises permanently shadowed landing sites on the Moon for NASA’s Artemis mission has been published.
  • [Jul 22] I successfully defended my PhD thesis, Physics-informed machine learning: from concepts to real-world applications.
  • [Feb 22] I was a course tutor and lead teaching assistant for the University of Oxford Department of Computer Science courses in Artificial Intelligence and Advanced Topics in Machine Learning.
  • [Dec 21] I will be interning half-time within NVIDIA’s Modulus team, contributing to their state-of-the-art scientific machine learning framework.
  • [Nov 21] Two NeurIPS workshop papers (here and here) accepted at The Symbiosis of Deep Learning and Differential Equations and Machine Learning and the Physical Sciences.
  • [Oct 21] My blog post on physics-informed neural networks hit 1M+ views! Thank you for all of the great conversation on this post!
  • [Sep 21] Our Nature Communications paper on peering into permanently shadowed regions on the Moon’s surface with machine learning is out. Read the press coverage from NASA, the Max Planck Institute and the University of Oxford here, here and here.
  • [Jul 21] Our latest ArXiv paper (and code) is out, which proposes FBPINNs: a scalable method for solving differential equations using physics-informed neural networks.
  • [Jul 21] Our Journal of The Royal Society Interface paper which localises elephants using their ground vibrations is out. Read the press coverage from the University of Oxford here.
  • [Jun 21] I will be returning to NASA’s Frontier Development Lab this year as a faculty member, helping to super-resolve images of the lunar surface.
  • [Mar 21] Our paper on denoising extremely low-light images of the lunar surface has been accepted at CVPR. Read my blog post here.
  • [Feb 21] I was a teaching assistant for the University of Oxford Department of Computer Science course in Advanced Topics in Machine Learning.
  • [Nov 20] I was a teaching assistant for the University of Oxford Centre for Autonomous Intelligent Machines and Systems course on Machine Learning.
  • [Sep 20] I helped to accelerate wind energy production in the UK by leading a team of 8 ML researchers at the UK Alan Turing Institute 2020 September Data Study Group.
  • [Aug 20] Our Solid Earth manifesto paper on using deep learning to accelerate seismic simulation has been published.
  • [Jul 20] Our Planetary Science Journal paper on using unsupervised machine learning to discover thermal anomalies on the Moon has been published.
  • [Jun 20] Our latest ArXiv paper is out, on using physics-informed neural networks to solve the wave equation.
  • [Feb 20] Our team won best technical solution at the Oxford Foundry AI & Climate Change Hackathon.
  • [Jan 20] I was invited to be a Series Editor for Elsevier’s 2020 Advances in Geophysics special issue on Machine Learning in the Geosciences.
  • [Jun 19] I will be joining NASA’s Frontier Development lab this summer as a AI researcher, working in their Moon For Good team.

Invited talks

  • [2023] Solving the wave equation with physics-informed neural networks: challenges and potential solutions.
    SIAM Conference on Computational Science and Engineering.
  • [2022] Scientific machine learning: ways to incorporate scientific principles into machine learning.
    University of Oxford / Lucerne University / University of Wyoming / Roche.
  • [2022] Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations.
    Brown University CRUNCH group.
  • [2022] Peering into permanently shadowed regions on the Moon using machine learning.
    American Geophysical Union, Session on Machine Learning and Data Science in Planetary Science / NASA/VIPER.
  • [2021] Poster presentation.
    NeurIPS, Workshop on Deep Learning for Differential Equations.
  • [2021] Scalable physics-informed deep learning for solving differential equations.
    NVIDIA Research / UK Science and Technology Facilities ​Council / University of Oxford Earth Sciences.
  • [2021] Poster presentation.
    CVPR main conference.
  • [2021] Machine learning for lunar exploration.
    UKRI Joint CDT Conference in Robotics and AI.
  • [2020] Poster presentation.
    NeurIPS, Workshop on Machine Learning and the Physical Sciences.
  • [2020] Solving the wave equation with physics-informed machine learning.
    Stanford Earth.
  • [2020] Anomaly detection on the lunar surface with variational autoencoders.
    ETH Zürich Machine Learning and Astrophysics workshop / UCLA Diviner seminar.
  • [2019] Poster presentation.
    NeurIPS, Workshop on Machine Learning for Geophysical Signals.

Recent publications

See full publication list

Key: plain language summary

Physics-informed machine learning: from concepts to real-world applications.
Investigated and discussed the challenges of scaling physics-informed machine learning (PIML) algorithms so that they can solve complex, real-world problems. Designed multiple different PIML algorithms for carrying out seismic simulation, low-light image enhancement, and thermal anomaly detection on the Moon.
Moseley, B. (2022).
University of Oxford.
Thesis

Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations.
Proposed a new method for solving differential equations which is able to scale to large problems by using physics-informed neural networks and a divide-and-conquer strategy.
Moseley, B., Markham, A., Nissen-Meyer, T. (2023).
(in review).
Paper    Code

Cryogeomorphic Characterization of Shadowed Regions in the Artemis Exploration Zone.
Used deep learning to enhance satellite images of proposed landing sites on the Moon for NASA’s crewed Artemis mission, in particular those which contain permanently shadowed regions, allowing us to see into them with unprecedented detail for the first time.
Bickel, V., Moseley, B., Hauber, E., Shirley, M., Williams, J.-P., Kring, D. (2022).
Geophysical Research Letters.
Paper    News article

Latest blog posts

See all blog posts

Latest comments