Matthew Ho 🚀

About Me

I’m a Member at the Institute for Advanced Study, exploring the intersection of artificial intelligence, astrophysics, and cosmology.

I build machine learning models that analyze a mix of observational and simulated data for inference and emulation. My research addresses a broad spectrum of problems, including cosmological galaxy clustering, galaxy formation, galaxy clusters, and dust attenuation. My research focuses on robust and reliable ML for science, using Bayesian statistics and explainable AI to build trust in complex models. I co-lead the Implicit Likelihood Inference group within the Learning the Universe collaboration.

My publications are on Google Scholar and my open-source projects are on GitHub. I’m always looking for new collaborators, so please feel free to send me an email!

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Interests
  • Astrophysics
  • Cosmology
  • Numerical Simulations
  • Machine Learning
  • Bayesian Statistics
Education
  • PhD Physics

    Carnegie Mellon University

  • MSc Machine Learning

    Carnegie Mellon University

  • MSc Physics

    Carnegie Mellon University

  • BSc Engineering Physics

    University of Illinois at Urbana-Champaign

Featured Publications
Recent Publications
(2026). CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations. arXiv e-prints.
(2026). Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations. The Astrophysical Journal.
(2026). The Deep Learning Halo Definer: A Multimodal Framework for Halo Mass and Gas Fraction Inference on Galaxy Groups and Clusters. arXiv e-prints.