Matthew Ho

Postdoctoral Researcher @astroIAP interested in cosmology, astronomy, statistics, and machine learning.

[Matthew Ho @MattHoCosmo]

Very proud to announce...

LtU-ILI, an all-in-one framework for ML parameter inference in astro/cosmo!💻🔭🌌 https://t.co/eYBF9ZWMNq

We unified the neural nets, training methods, and validation metrics used at the forefront of ML+astro in one accessible package... (1/9)

[Matthew Ho @MattHoCosmo]

@TriAstroML Congrats Tri!

[Matthew Ho @MattHoCosmo]

For more info, checkout:
- the code (https://t.co/eYBF9ZWMNq)
- the paper (https://t.co/JAfdyPcTfu)

Of course, with any new code, we want your feedback, so don't hesitate to request new features or point out 🐛's on the Github. :)
(9/9)

[Matthew Ho @MattHoCosmo]

Very proud to announce...

LtU-ILI, an all-in-one framework for ML parameter inference in astro/cosmo!💻🔭🌌 https://t.co/eYBF9ZWMNq

We unified the neural nets, training methods, and validation metrics used at the forefront of ML+astro in one accessible package... (1/9)

[Matthew Ho @MattHoCosmo]

Lastly, a cool demonstration of combined data constraints:

Three trials infer galactic dust parameters, from either the [g-r] colors (blue), the r-band (orange), or both (green). Using the combination of data results in much tighter constraints than using each separately. (8/9)

[Matthew Ho @MattHoCosmo]

@MinhNguyenAstro @bwandelt Available here! https://t.co/SNubnLm90c

[Benjamin Wandelt @bwandelt]

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. @MattHoCosmo

https://t.co/j7TBXmgGic

[Benjamin Wandelt @bwandelt]

What if we could use machine learning to turn information into insights?

Our new paper, led by the outstanding Matt Ho @MattHoCosmo discovers key degrees of freedom for describing data, ordered by importance.

It is now on the arXiv:
https://t.co/pSX0awScIp

1/5

[Benjamin Wandelt @bwandelt]

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:

https://t.co/2d5he052pL

Led by Ronan Legin, @MattHoCosmo

[Yann LeCun @ylecun]

The Horn Antenna in Holmdel, NJ was used by Nobel laureates and Bell Labs scientists A. Penzias & B Wilson to discover the cosmic background radiation.

This petition is asking the town to turn the site into a park & not sell it to a developer.
https://t.co/etPOUwxZYE

[IAP @astroIAP]

🫂Welcome to the newcomers of 2022: PhD students, post-docs and engineers @astroIAP🌟! @CNRS_Paris @INSU_CNRS @Sorbonne_Univ_ @ScienceSorbonne

[Matthew Ho @MattHoCosmo]

New desktop wallpaper!

[NASA Webb Telescope @NASAWebb]

🌟 A star is born!

Behind the curtain of dust and gas in these “Cosmic Cliffs” are previously hidden baby stars, now uncovered by Webb. We know — this is a show-stopper. Just take a second to admire the Carina Nebula in all its glory: https://t.co/tlougFWg8B #UnfoldTheUniverse

[NASA @NASA]

It's here–the deepest, sharpest infrared view of the universe to date: Webb's First Deep Field.

Previewed by @POTUS on July 11, it shows galaxies once invisible to us. The full set of @NASAWebb's first full-color images & data will be revealed July 12: https://t.co/63zxpNDi4I

[Nature Astronomy @NatureAstronomy]

In 1933, Zwicky’s measurement of the Coma cluster's mass led him to infer the existence of dark matter. @MattHoCosmo, @astro_michelle et al. have applied a new DL approach to revisit Zwicky's measurement and demonstrate the progress of modern astronomy. https://t.co/ddMykVvl0I

[Markus Deserno @MarkusDeserno]

The PhD candidates are ready! So freakin’ proud of all of them! 🎉
#CMUgrad #tartanproud