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#47 Bayes in Physics & Astrophysics, with JJ Ruby

#47 Bayes in Physics & Astrophysics, with JJ Ruby

FromLearning Bayesian Statistics


#47 Bayes in Physics & Astrophysics, with JJ Ruby

FromLearning Bayesian Statistics

ratings:
Length:
76 minutes
Released:
Sep 21, 2021
Format:
Podcast episode

Description

The field of physics has brought tremendous advances to modern Bayesian statistics, especially inspiring the current algorithms enabling all of us to enjoy the Bayesian power on our own laptops.
I did receive some physicians already on the show, like Michael Betancourt in episode 6, but in my legendary ungratefulness I hadn’t dedicated a whole episode to talk about physics yet.
Well that’s now taken care of, thanks to JJ Ruby. Apart from having really good tastes (he’s indeed a fan of this very podcast), JJ is currently a postdoctoral fellow for the Center for Matter at Atomic Pressures at the University of Rochester, and will soon be starting as a Postdoctoral Scholar at Lawrence Livermore National Laboratory, a U.S. Department of Energy National Laboratory.
JJ did his undergraduate work in Astrophysics and Planetary Science at Villanova University, outside of Philadelphia, and completed his master’s degree and PhD in Physics at the University of Rochester, in New York.
JJ studies high energy density physics and focuses on using Bayesian techniques to extract information from large scale physics experiments with highly integrated measurements.
In his freetime, he enjoys playing sports including baseball, basketball, and golf.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) !
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin and Cameron Smith.
Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
Center for Matter at Atomic Pressures: https://www.rochester.edu/cmap/ (https://www.rochester.edu/cmap/)
Laboratory for Laser Energetics: https://www.lle.rochester.edu/index.php/about-the-laboratory-for-laser-energetics/ (https://www.lle.rochester.edu/index.php/about-the-laboratory-for-laser-energetics/)
Lawrence Livermore National Laboratory: https://www.llnl.gov/ (https://www.llnl.gov/)
JJ's thesis -- Bayesian Inference of Fundamental Physics at Extreme Conditions: https://www.lle.rochester.edu/media/publications/documents/theses/Ruby.pdf (https://www.lle.rochester.edu/media/publications/documents/theses/Ruby.pdf)
Recent Fusion Breakthrough: https://www.llnl.gov/news/national-ignition-facility-experiment-puts-researchers-threshold-fusion-ignition (https://www.llnl.gov/news/national-ignition-facility-experiment-puts-researchers-threshold-fusion-ignition)
LBS #6, A principled Bayesian workflow, with Michael Betancourt: https://www.learnbayesstats.com/episode/6-a-principled-bayesian-workflow-with-michael-betancourt (https://www.learnbayesstats.com/episode/6-a-principled-bayesian-workflow-with-michael-betancourt)
20 Best Statistics Podcasts of 2021: https://welpmagazine.com/20-best-statistics-podcasts-of-2021/ (https://welpmagazine.com/20-best-statistics-podcasts-of-2021/)
E.T. Jaynes, Probability Theory -- The Logic of Science: https://www.goodreads.com/book/show/151848.Probability_Theory...
Released:
Sep 21, 2021
Format:
Podcast episode

Titles in the series (100)

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Paris. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy