Matthew Ho
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    • Ordered embeddings and intrinsic dimensionalities with information-ordered bottlenecks
    • Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling
    • Cosmology with One Galaxy: Autoencoding the Galaxy Properties Manifold
    • RTFAST-Spectra: emulation of X-ray reverberation mapping for active galactic nuclei
    • Learning the Universe: $3 h^-1mฬŠ Gpc$ Tests of a Field Level $N$-body Simulation Emulator
    • Learning the Universe: physically-motivated priors for dust attenuation curves
    • Learning the Universe: $3$backslash$h^$$-1$$$$$backslash$rm Gpc$$ $ Tests of a Field Level $ N $-body Simulation Emulator
    • Sensitivity analysis of simulation-based inference for galaxy clustering
    • Sensitivity analysis of simulation-based inference for galaxy clustering
    • Bye-bye, Local-in-matter-density Bias: The Statistics of the Halo Field Are Poorly Determined by the Local Mass Density
    • Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
    • CHARM: Creating Halos with Auto-Regressive Multi-stage networks
    • Metallicity Dependence of Pressure-regulated Feedback-modulated Star Formation in the TIGRESS-NCR Simulation Suite
    • Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics
    • LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
    • Building Trustworthy Machine Learning Models for Astronomy
    • Information-Ordered Bottlenecks for Adaptive Dimensionality Reduction
    • MNRAS: Letters 527
    • Posterior sampling of the initial conditions of the universe from non-linear large scale structures using score-based generative models
    • Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
    • Benchmarks and explanations for deep learning estimates of X-ray galaxy cluster masses
    • Information-Ordered Bottlenecks for Adaptive Semantic Compression
    • A Machine-learning Approach to Enhancing eROSITA Observations
    • The dynamical mass of the Coma cluster from deep learning
    • CLMM: a LSST-DESC cluster weak lensing mass modeling library for cosmology
    • Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
    • Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
    • Aging haloes: implications of the magnitude gap on conditional statistics of stellar and gas properties of massive haloes
    • A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
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    • ltu-ili
    • cuboid_remap_jax
    • pytorch-iobs
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ltu-ili

Jul 2, 2025 ยท 1 min read
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An all-in-one framework for machine learning parameter inference in astrophysics and cosmology.

Last updated on Jul 2, 2025
Matthew Ho
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Matthew Ho
Postdoctoral Researcher
cuboid_remap_jax Jun 29, 2025 →

ยฉ 2025 Me. This work is licensed under CC BY NC ND 4.0

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