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A show about the science and engineering behind AutoML.
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Contact path
Public web form
Booking probability
35%
Guest openness
Selective
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Signup to Generate a PitchRecent episodes include "MLGym: A New Framework and Benchmark for Advancing AI Research Agents" (31 October 2025, 89 minutes), "Leverage Foundational Models for Black-Box Optimization" (22 September 2025, 57 minutes), and "Nyckel - Building an AutoML Startup" (7 March 2025, 81 minutes).
The AutoML Podcast publishes roughly every few months, with episodes averaging 70 minutes across 43 episodes.
Booking probability for The AutoML Podcast is 35%, and the host is rated Selective on guests. The AutoML Podcast requires a Pod Score of 80 or higher. The AutoML Podcast's contact route on record: public web form.
The strongest fits for The AutoML Podcast are IT Services & Software Development. The AutoML Podcast has a technology audience.
The AutoML Podcast is a technology podcast with 43 episodes published.
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The AutoML Podcast
AutoML is dead an LLMs have killed it? MLGym is a benchmark and framework testing this theory. Roberta Raileanu and Deepak Nathani discuss how well current LLMs are doing at solving ML tasks, what the biggest roadblocks...
The AutoML Podcast
Where and how can we use foundation models in AutoML? Richard Song, researcher at Google DeepMind, has some answers. Starting off from his position paper on leveraging foundation models for optimization, we chat about...
The AutoML Podcast
Oscar Beijbom is talking about what it's like to run an AutoML startup: Nyckel. Beyond that, we chat about the differences between academia and industry, what truly matters in application and more. Check out Nyckel at...
The AutoML Podcast
Colin White, head of research at Abacus AI, takes us on a tour of Neural Architecture Search: its origins, important paradigms and the future of NAS in the age of LLMs. If you're looking for a broad overview of NAS...
The AutoML Podcast
There are so many great foundation models in many different domains - but how do you choose one for your specific problem? And how can you best finetune it? Sebastian Pineda has an answer: Quicktune can help select the...
The AutoML Podcast
Designing algorithms by hand is hard, so Chris Lu and Matthew Jackson talk about how to meta-learn them for reinforcement learning. Many of the concepts in this episode are interesting to meta-learning approaches as a...
The AutoML Podcast
AutoML can be a tool for good, but there are pitfalls along the way. Rahul Sharma and David Selby tell us about how AutoML systems can be used to give us false impressions about explainability metrics of ML systems...
The AutoML Podcast
In today's episode, we're introducing the very special Theresa Eimer to the show. Theresa will be taking over the hosting of many of the future episodes. Theresa has already recorded multiple episodes and we are stoked...
The AutoML Podcast
Today we're talking with Nick Erickson from AutoGluon. We discuss AutoGluon's fascinating origin story, its unique point of view, the science and engineering behind some of its unique contributions, Amazon's Machine...
The AutoML Podcast
Today we're talking with Joseph Giovanelli about his work on integrating logic and argumentation into AutoML systems. Joseph is a PhD student at the University of Bologna. He was more recently in Hannover working on...
The AutoML Podcast
Today we're talking with Caitlin Owen, a post-doc at the University of Otago about her work on error decomposition. She recently published a paper titled "Towards Explainable AutoML Using Error Decomposition" about how...
The AutoML Podcast
Today we're talking with Gaurav Rao, the EVP & GM of Machine Learning and AI at AtScale, a company centered around the semantic layer. For some time now, I've been feeling that there is a deep connection between a...
The AutoML Podcast
Today Ankush Garg is speaking with Rishi Bommasani, PhD student at Stanford and one of the originator of the term Foundation Models. They’re talking about the origins of the term Foundation Model, which he and his group...
The AutoML Podcast
Today we're talking with Raymond Peck, a senior engineer and director in the AutoML space. He spent time at H2O, dotData, Alteryx and many other places. This is a fascinating conversation about the business...
The AutoML Podcast
Today we’re talking to Noah Hollmann and Samuel Muller about their paper on TabPFN - which is an incredible spin on AutoML based on Bayesian inference and transformers. [Quick note on audio quality]: Some of the tracks...
The AutoML Podcast
Today we’re talking to Sean Sexton, the Director of Modeling and Analytics Consulting at KPMG, about the role of models in financial institutions and how the risks associated with them is managed. This turned out to be...
The AutoML Podcast
Today we’re talking to Matt Levine. Matt is a PhD student in computing and mathematical sciences at Caltech, and he focuses on improving the prediction and inference of physical systems by blending together both...
The AutoML Podcast
Today we’re chatting with Junhong Shen, a PhD student at Carnegie Mellon. Junhong and her team are working on the generalizability of NAS algorithms across a diverse set of tasks. Today we'll be talking about DASH, a...
The AutoML Podcast
Today we're speaking with Marius Lindauer and it is certainly one of my favorite episodes! As you’ll hear, Marius is full of ideas for where AutoML systems can and should go. These ideas are crystallized in a blog-post...
The AutoML Podcast
Today Ankush Garg is talking to Mehdi Bahrami about his recent project: BERT-Sort. BERT-Sort is an example of how large language models can add useful context to tabular datasets, and to AutoML systems. Mehdi is a...
Recent guests on The AutoML Podcast. Study who booked and why before you pitch.
Portfolio Manager and CEO, Verecan Capital Management
Ambassador, Usobi
William D. Eberle Professor of Economics at Stanford University, Basketball and a Dream Foundation
Barbara and Stephen Friedman Professor and Chair of the Department of Emergency Medicine, CSIR–National Institute of Oceanography
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