About Me

Education: BSc in Astronomy & Physics at University of Toronto (2017-2021)
PhD in Astronomy at Tohoku University (東北大学) (2022-2027)

Research Interests: Galaxy Evolution and Formation, Galaxy Morphology, Cosmology, Machine Learning Techniques, Simulation Based Inference, Computational Astrophysics, Physics Informed Neural Networks, SED Fitting

Nationality: Venezuela 🇻🇪/Canada 🇨🇦

Research

Why do galaxies look the way they do? is the fundamental question my research tries to answer. I investigate how galaxy formation and evolution are imprinted on their morphological structures. Currently, I develop machine learning and simulation-based inference techniques—leveraging frameworks like PyTorch and implicit likelihood inference—to bridge large-scale observational surveys and high-resolution hydrodynamical simulations.

Core Research Areas:

  • Katachi (形) & Katachi-PRIME: Developing interpretable Vision Transformer and CNN frameworks to infer full posteriors for galaxy physical parameters and decode the signatures of past star formation.
  • Simulation & Cosmological Modeling: Analyzing dark matter halo properties, galaxy kinematics, and morphology connections using hydrodynamical simulation suites, including IllustrisTNG and DREAMS.
  • Observational Morphology: Studying the structural evolution of galaxies targeted by wide-field observational missions, including the Euclid space telescope.

Publications: NASA ADS ORCID Google Scholar

Click here to check out an AI-generated podcast clip that describes the Katachi (形) method I developed to better understand galaxy structure formation.