Summary
Galactic archaeology, stellar populations and machine learning applied to astronomical data.
My work sits at the intersection of computational methods and astrophysical problems, focused on galactic archaeology, stellar populations, and the structure of the Milky Way.
Areas of Interest
Astrophysics
- Galactic archaeology and stellar populations
- Milky Way structure and chemical evolution
- Large-scale astronomical data analysis
Computer Science
- Scientific computing and data pipelines
- Machine learning applications in astronomy
- Reproducible computational workflows
Projects
- Anomaly Detection in Gaia Data — Building a machine learning pipeline to spot rare stellar objects across Gaia and complementary surveys.
- Understanding Dark Matter through Extragalactic Shocks — Testing the accuracy of a Monte Carlo method for dating galaxy cluster collisions, as an indirect proxy for dark matter’s behavior.
- Simulating the Impact of Satellites on Astronomical Observations — An AI algorithm for removing artificial satellite trails from astronomical images, recovering 99.7% of the lost information.