GRADuateML Podcast

GraduateML

5.0 (3)
Education
Weekly

I talk to cool people about machine learning.

Oct 17

The Un-expectedly Deep Statistics of Machine Learning - Michael O'Connor

I had the absolute pleasure of inviting Michael O'Connor onto the show to ask the deeper questions of how statistics relates to machine learning/deep learning, how neural networks can be causally interpreted, and why you're actually not ever as smart as you think you are. I think there are some gems of advice for people who want to know how to approach understanding machine learning ranging anywhere from beginner to advanced.If you want to check out Michael's stuff:IG: moconnor1611oconnor.mw@ufl.eduIf you want me to teach you machine learning:https://www.skool.com/graduateml/about
Oct 16

One Must Imagine Sisyphus Prompting An LLM - Nick Chin

I had the absolute pleasure of sitting down to talk with the absolute genius that is Nick Chin. Versed in everything from environmental science to machine learning he walks you through the idea of red tide, the idea behind model interpretability, and most pressingly the existential horror of AI that wasn't even predicted in life 3.0
Oct 16

The Obscene Cost of Rendering Video Game Clouds - Michael Tomodakis

Join me as I interview a man who has spent a ridiculous portion of his life attempting to optimize the rendering of video game clouds. From Red Dead Redemption 2, to Ghost Recon Wildlands, to any flight simulator you've ever played, these games all have clouds, and the animation budget knows it better than all of us. It was an absolute pleasure getting to pick Michael's brain about the machine learning implications of this from learned gaussians to neural radiance fields (NERFs) and I hope to do it again someday.A big thanks to Michael for his insane depth of knowledge and willingness to hop on the show.
Oct 16

Can 3 Grad Students Translate Better Than Tech Giants? - Americas NLP 2026 Winners

I am grateful to have gotten to speak with the winners of the Americas NLP 2026 challenge. They are incredibly well versed in their fields and managed to get a superior result with very pressing time and resource constraints. Dzmitry Kasinetsdkasinets@ufl.eduChristopher Driggers-Ellis driggersellis.cw@ufl.eduAashish Dhawanaashish.dhawan@ufl.eduChris’s Lab website:https://ufdatastudio.comAashish & Dzmitry’s Lab website:https://dsr.cise.ufl.eduPapers:AmericaNLP:https://aclanthology.org/2026.america...WMT26:https://arxiv.org/abs/2608.13722
Oct 12

This Hardware Might Beat The Transistor - Gabriel Fitt

Gabriel Fitt is a nerd and a researcher in the field of electrical engineering. His takes on analog computing interested me from the moment I met him. The guy has some serious philosophical intellect and I'm glad I got to sit down and do a pod with a fellow geek. You should expect to learn about cross bar arrays and how analog computation/memristors store information similarly to the brain in certain representational frameworks. The emergent properties of memory in systems are fascinating.
Oct 12

You CAN Train Without BackPropagation - JingWei Sun

This is an episode I did with the absolute prodigy Jingwei Sun. Not only has he published in both NeurIPS and ICML, but he is also responsible for some of the most fascinating work on the matter of edge compute and federated learning. I'm very grateful I got to do an episode with him to dive deeper into his research.
Sep 26

Modern AI is Hardware Limited - Maxime Vaillant

I had the absolute pleasure of getting to speak with AI researcher Maxime Vaillant about neuromorphic compute, spiking neural networks, the hardware/software bottlenecks of AI, and how the future of AI looks in the coming years. Absolutely love this guy and his work. Go checkout spikeCLR aka EventCLR.

7 Episodes

5

out of 5

3 Ratings

I talk to cool people about machine learning.

Creator

GraduateML
Years Active

2026
Episodes

7
Rating

Clean
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© GraduateML
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GRADuateML Podcast