Oxford Research Software Engineering

Support for the Schmidt AI in Science Fellows

Fellows are offered support from dedicated Research Software Engineers (RSEs) within the Oxford RSE Group, which cover:

  • Immediate support on acute software problems, e.g. getting software installed and running on the Advanced Research Computing service (ARC)
  • Longer term support on ongoing software problems or long-term questions
  • Active development support for fellows' software projects

Currently supported projects include:

  • Bee Behaviour Tracking (Rachel Parkinson): A deep learning system to track bees in experimental chambers in order to analyse how they move and interact with one another. The research aim is to analyse the effects of pesticides on their behaviour.
  • SpeedyWeather.jl (Milan Klöwer): A global atmospheric model with simple physics developed as a research playground with an everything-flexible attitude as long as it is speedy. It is easy to use and easy to extend, making atmospheric modelling an interactive experience.
  • What We Dont Catalog (Micah Bowles): A project that aims to find unrecorded data features in galaxy catalogues. Work has investigated various methods of visualising the latent space embeddings of autoencoding deep neural networks to be able to understand the feature space.
  • Reef Motion 3D (Cait Newport): A project that studies the behaviour of triggerfish in the wild. To analyse the behaviour of these fish from underwater videos, deep learning object detection and tracking algorithms are being evaluated to follow how they move around coral reefs.
  • Morphing Birds (Lydia France): A project to visualise the principal components of bird flight using data collected using motion capture techniques. To disseminate the results of the research to the wider community work has been done to create webpages containing animated graphs where viewers can select individual components in isolation and see how they relate to movement of a model.
  • Meta OpSim (Mengyun Wang): A new project involving Photonics and metasurface optimisation. This is in two strands, one implementing a new field model into an existing metasurface optimiser and the other exploring the possibility of replacing the optimiser entirely with some form of deep learning.

RSEs who support Schmidt Fellows:

Jack Leland

Oliver King