Astrophysics & Machine Learning Researcher
Ph.D. Candidate @ The Australian National University
I work at the intersection of stellar astrophysics, radiative transfer, and machine learning. My research focuses on modeling inhomogeneous stellar surfaces, developing high-performance spectral synthesis tools, and building next-generation ML-based emulators for stellar spectroscopy.
SPICE is a JAX-based framework for simulating stellar spectra that includes stellar surface inhomogeneities, such as spots, temperature gradients, pulsations, and binary effects.
The software combines a machine-learning model Transformer Payne (Różański et al., 2024) with a 3D stellar mesh system to efficiently model complex surface phenomena. Key features include:
Please reach out if you have any ideas or suggestions, as well as feature requests! I am happy to include more features, as well as help you integrate SPICE into your research. We can also discuss creating custom spectral models for your needs.
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You can view my CV below, or download the PDF.