Andrew Casey

Associate Professor Andrew Casey is a computational astrophysicist at Monash University's School of Physics and Astronomy, specializing in data-driven methods applied to astrophysical problems. He holds a PhD from Australian National University (ANU), with postdoctoral research at the University of Cambridge as a King's College Senior Member. His work bridges astrophysics, statistics, and machine learning, focusing on stellar astrophysics, Bayesian inference, and large-scale surveys like Gaia and GALAH. He leads projects on stellar interiors, galactic evolution, and unresolved binary systems. Casey has received awards including the Young Tall Poppy Award and the Australian Prime Minister's Endeavour Award. He actively mentors PhD students in data analysis and computational methods.

Education: PhD in Astronomy & Astrophysics (ANU), Visiting Graduate Student at MIT (via Endeavour Award).

Research interests include: computational data analysis, stellar astrophysics, Bayesian statistics, machine learning applications in astronomy, and algorithm optimization. Notable projects include GaiaUnlimited (studying Milky Way disc structure) and GALAH surveys (chemical tagging of stars).

Recent articles focus on topics like Gaia data analysis, exoplanet detection via astrometry, and models for M dwarfs. His work often emphasizes interdisciplinary approaches, combining physics-based models with AI-driven techniques.

Awards: Young Tall Poppy Award (2018), Fresh Science (2018), Senior Member (2015), Australian Prime Minister's Endeavour Award (2011), and InnovationACT Prize (2011).

Advising and grants: Accepting PhD students; leads ARC-funded projects such as the Discovery Early Career Researcher Award. Projects include Gaia survey analysis, star formation studies, and binary star detection.

Labs/Teams: Involved with the Monash Astrophysics Group and collaborates internationally on surveys like Gaia and SDSS-V.