Ep. 31 - Harvard Astrophysics: Machine Learning & Interstellar Dust Clouds ft. Andrew Saydjari Abstract: The Future of Science

Our guest this week, Andrew Saydjari, is midway through his PhD in Astrophysics at Harvard University. Andrew's research lies at the intersection of Astrophysics and Machine Learning, and he's studying the massive dust clouds in our very own galaxy. Tune in to tap into the wealth of knowledge that Andrew's bringing to Episode 31!

On this week's episode we answer questions like: 

Why should you care about interstellar dust clouds that are a million times as wide as the earth's orbit around the sun?
What do spectrums of light tell us about the molecular make-up of these clouds?
How much information can I glean from just a single image of a molecular cloud out there in space?
And how does the symmetry of molecules factor into all this?

Topics & Concepts
Data Collection in the Physical Sciences
Group Theory & Symmetries
Spectroscopy, Optics & Lasers
Rotating Bodies & Moments of Intertia
Space Dust: Building Blocks of The Universe
Relative Size & Scales of the Universe
Nebulae & Infrared Imaging
Fluid Dynamics, Simulations
Hot Gases & Plasma
Isotropy
Magnetic Fields & Symmetry-Breaking
JPEG Compression
Fourier Transforms
Heisenberg's Uncertainty Principle
Wavelets

Further Reading - Related Research
https://arxiv.org/abs/2010.11963

VIA (Virtues in Action) - 24 Character Strengths Survey
https://www.viacharacter.org/account/register


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