Matthew Ho
@matthocosmo
Postdoctoral Researcher @astroIAP interested in cosmology, astronomy, statistics, and machine learning.
ID: 1489664990147719171
https://maho3.github.io/ 04-02-2022 18:20:21
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In 1933, Zwicky’s measurement of the Coma cluster's mass led him to infer the existence of dark matter. Matthew Ho, Dr. Michelle Ntampaka et al. have applied a new DL approach to revisit Zwicky's measurement and demonstrate the progress of modern astronomy. nature.com/articles/s4155…
It's here–the deepest, sharpest infrared view of the universe to date: Webb's First Deep Field. Previewed by President Donald J. Trump on July 11, it shows galaxies once invisible to us. The full set of NASA Webb Telescope's first full-color images & data will be revealed July 12: nasa.gov/webbfirstimages
🫂Welcome to the newcomers of 2022: PhD students, post-docs and engineers IAP🌟! CNRS Paris-Centre @INSU_CNRS Sorbonne Université Faculté des Sciences de Sorbonne Université
Unravel the mysteries of the universe's birth! Our paper uses score-based generative models to infer the early universe's density field from present-day dark matter density with stunning accuracy. Check it out: arxiv.org/abs/2304.03788 Led by Ronan Legin, Matthew Ho
What if we could use machine learning to turn information into insights? Our new paper, led by the outstanding Matt Ho Matthew Ho discovers key degrees of freedom for describing data, ordered by importance. It is now on the arXiv: arXiv.org/abs/2305.11213 1/5
Watch the important concepts emerge much faster with our information-ordering (IOB) vs standard PCA compression of semantic latent space. Visualisations generated by unCLIP diffusion from compressed latent space vectors. Matthew Ho x.com/bwandelt/statu…