Supervisor for student research projects:

  • 2019/20: Proposal and supervision of an undergraduate honours (senior) thesis at the University of Edinburgh. In developing and applying a genetic programming toolset for analytic formalism recovery via symbolic regression, the project targets the extraction of descriptive relationships between properties relevant to the evolution of galaxies in a bid to explore the dynamics of cosmological simulations through a process steered by artificial intelligence.
  • 2018/19: Proposal and supervision of an undergraduate summer research project at the Royal Observatory, Edinburgh. The project extends and merges different types of generative adversarial networks to create high-quality synthetic galaxy images for the calibration of shape measurement algorithms in weak lensing and dataset augmentation for deep learning approaches to galaxy classification and deblending. The resulting paper is published in MNRAS, Vol. 485(3), pp. 3203-3214.

TA at the School of Physics and Astronomy:

  • 2019/2020: Discovering Astronomy (PHYS08039)
  • 2018/2019: Programming, Data Analysis and Machine Learning 2 (PGPH11102)
  • 2018/2019: Programming, Data Analysis and Machine Learning 1 (PGPH11102)
  • 2017/2018: Introductory Astrophysics (PHYS08050)

Guest lecturer for the Programming Society:

  • 2017/18: Introduction to Machine Learning, covering the implementation of naive Bayes classifiers
  • 2017/18: Building Probabilistic Simulations, covering Bayes’ theorem and the Monty Hall problem

Outreach in astrophysics and data science:

  • ROE Open Days, opening the Royal Observatory to the public with a program of tours and lectures
  • DataFest 2019 - Scotland’s Festival of Data Innovation, talking about data science in cosmology