Richard Galvez

@richardgalvez

Theoretical cosmologist/astrophysicist, living on the boundary between astrophysics and data science. MSDS, Faculty Fellow

New York, NY
Vrijeme pridruživanja: studeni 2009.

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  1. Prikvačeni tweet
    15. ožu 2019.

    As a hobby, I started a "data science comedy" blog analyzing . I most recently took a look at the films permits dataset here: . "The Netflix Effect" plot is attached-- check it out!

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  2. proslijedio/la je Tweet
    3. velj

    What's another name for a supernova? A POPstar. HAHAHAHA. Living is pain. Unbearable. End me.

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  3. proslijedio/la je Tweet
    4. velj

    Today on : binary stars from ! Figure 5 is my fav.: (a) The close binary fraction decreases w/ increased primary-star size (lower logg)! This is presumably because closer companions are consumed as the size of the primary gets larger,

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  4. 3. velj
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  5. 30. sij
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  6. 29. sij

    As a non medical professional, I apologize in how naive this comment may be, but if data on virus sequencing and their vaccines exists, a deep learning approach could accelerate vaccine development for the new

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    28. sij

    I'm shocked to announce I now have a parody account ...there are two or three suspects who will be revealed in the next days following a thorough investigation. Stay tuned!

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  8. proslijedio/la je Tweet
    25. sij

    Giacconi once called me a fascist in front of the entirety of a Hubble Fellow Symposium for... advocating for better policies for fellows with families. Never had met him. We often lionize departed scientists but that dude was an asshole. Don’t publicly shit on junior people.

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  9. proslijedio/la je Tweet
    21. sij
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  10. proslijedio/la je Tweet
    22. sij

    Our “mining gold from implicit models to improve likelihood-free inference” paper was finally accepted to PNAS! 🎉 It was an epic adventure in publishing cross-disciplinary research Johann Brehmer

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  11. proslijedio/la je Tweet
    21. sij

    Today's Astro Data Group blog post is by . He is working to quantify our qualitative descriptions of turbulent structure:

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  12. proslijedio/la je Tweet
    20. sij

    Given the smoothness of videos, can we learn models more efficiently than with ? We present Sideways - a step towards a high-throughput, approximate backprop that considers the one-way direction of time and pipelines forward and backward passes.

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  13. 19. sij
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  14. 19. sij

    Interested phd students and postdocs:

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  16. 19. sij
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  17. proslijedio/la je Tweet
    14. pro 2019.

    My talk video and slides are up here starting at 43:35 I explain our work extracting learned physical laws from graph networks in . I try to make it digestible for a broad audience!

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  18. proslijedio/la je Tweet
    5. sij

    At Jan 7th I’ll present a poster on normalizing flows for learning flexible and scalable color-magnitude diagrams from noisy parallax and photometry data. Work with , Lauren Anderson, , . Paper here:

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  19. 4. sij

    This is a phenomenal opportunity for graduate students. If you read this and you’re a grad student, please consider going!

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  20. 1. sij

    Unfortunately very true, and I don’t believe we could have done it any differently. The downfall of intelligent life all over the universe is the entropy it creates

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  21. proslijedio/la je Tweet
    31. pro 2019.

    In an effort to support the Australian response, the Japanese have switched the spacecraft to a faster cadence, taking pictures every 2.5 minutes 6/N

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