CV

Research & work experience

  • Quantitative Researcher, Auros, London Jul 2025 – Sep 2026Built the research pipelines and the monitoring behind automated trading strategies, and the analysis that told us whether they were working. Full-depth limit-order-book features and short-horizon predictive signals; execution research from decision-time markouts to live fill quality; exchange ingress latency, venue microstructure and colocation economics; experimentation with deep learning.
  • Postdoctoral Researcher, Imperial College London 2023 – 2025Searched for high-z quasars in the Euclid survey using a Bayesian framework, and developed the automated end-to-end pipeline behind it: survey-tile acquisition and preprocessing, template fitting and scoring, validation against known objects, and a web console for vetting candidates. How it works.
  • Chief Data Scientist / Head of Research, Superestate / Vali, Sydney 2020 – 2022Designed the infrastructure, databases and machine learning behind an automated property valuation model, used to buy property for the superannuation fund and released publicly as Vali: an on-the-fly valuation platform on AWS, orchestrated with Prefect. Led a team of two data scientists.
  • Quantitative Analyst, then Senior Quantitative Analyst, Hometrack (REA Group), Sydney 2013 – 2020Designed and implemented statistical models of the property market, and the production pipelines that served them to clients. Maintained the SQL Server databases behind them.
  • Casual Lecturer and Tutor, Macquarie University, Sydney 2017 – presentLecturer in business analytics; tutor and marker for astronomy, mathematical economics and statistics.
  • Research Assistant, CSIRO, Sydney 2015 – 2016Built a data reduction pipeline for ATCA; observed remotely with Parkes and ATCA from Marsfield.

Publications

First author

  • The GALAH Survey: a new sample of extremely metal-poor stars using a machine-learning classification algorithm. Hughes, A. C. N., Spitler, L. R., Zucker, D. B., et al. The Astrophysical Journal, 2022. arXiv · doi
  • Quasar and galaxy classification using Gaia EDR3 and CatWISE2020. Hughes, A. C. N., Bailer-Jones, C. A. L., Jamal, S. Astronomy & Astrophysics 668, A99, 2022. arXiv · doi

Co-author

  • Euclid: discovery of 31 new quasars at 6.6 < z < 7.8. Yang, D., Hennawi, J. F., Guarneri, F., et al. Astronomy & Astrophysics 711, A104, 2026. arXiv · doi
  • Spectroscopic follow-up of statistically selected extremely metal-poor star candidates from GALAH DR3. Da Costa, G. S., Bessell, M. S., Nordlander, T., et al. Monthly Notices of the Royal Astronomical Society, 2023. arXiv · doi
  • Euclid. I. Overview of the Euclid mission. Euclid Collaboration. Astronomy & Astrophysics 697, A1, 2025. arXiv
  • Euclid: Early Release Observations. NISP-only sources and the search for luminous z = 6–8 galaxies. Weaver, J. R., et al. Astronomy & Astrophysics 697, A16, 2025. arXiv
  • Euclid: Early Release Observations. A preview of the Euclid era through a galaxy cluster magnifying lens. Atek, H., et al. Astronomy & Astrophysics 697, A15, 2025. arXiv

Tools

  • Python, R, SQL, C++. PyTorch and TensorFlow. Docker, AWS and GCP, Prefect, DuckDB, SQL Server.
  • Certified in data engineering with GCP, applied machine learning in Python, and convolutional networks and deployment with TensorFlow.

Education

  • PhD, Max Planck Institute for Astronomy, Heidelberg, and Macquarie University, Sydney 2019 – 2023Statistical methods and large astronomical surveys. Supervised by Coryn Bailer-Jones and Daniel Zucker.
  • Master of Research, Macquarie University 2016 – 2017Needle in a haystack: advanced statistical techniques and large stellar spectroscopic datasets.
  • BSc, Astronomy & Astrophysics, Macquarie University 2013 – 2015Completed part-time while working as a quantitative analyst.
  • BA Mathematics / BEc Honours, Econometrics, Macquarie University 2007 – 2012Fractional cointegration between Australia and its neighbouring countries: an analysis of stock indices.

Talks & posters

  • 2022European Astronomical Society meeting, Valencia: using machine learning to identify extremely metal-poor stars with GALAH. Guest lecture, Masters of Analytics, Macquarie.
  • 2021Astronomical Society of Australia AGM: searching for extremely metal-poor stars with GALAH, a how-to guide. First Stars group meeting. Guest lecture, Macquarie.
  • 2020Guest lecture, Masters of Analytics, Macquarie.
  • 2017Machine Learning in Astronomy, Sydney, and Macquarie R Users Group: the versatility of R, from astrophysics to horse racing to the property market. ASA AGM, Canberra: poster and sparkler talk on the GALAH survey and machine learning. Physics and Astronomy Computing Group: introduction to R. Research Frontiers poster sessions: an introduction to t-SNE.

Conferences & workshops

  • European Astronomical Society meeting, Valencia, July 2022.
  • Statistical Challenges in Modern Astronomy VI, online, June 2021.
  • ESO/NEON Observing School, La Silla, Chile, March 2018.
  • A Celebration of CEMP and Gala of GALAH, Monash, November 2017.
  • Astronomical Society of Australia AGM and Harley Wood Winter School, ANU, July 2017.