# Sébastien Bubeck

> The mathematician who spent years refining the theory of how machines learn from choices, then turned that precision on the black box itself - cracking open transformer models to see what makes them tick.

*Researcher in machine learning*

**Canonical URL:** https://fame.am/person/sebastien-bubeck

## Identity
- **Profile type:** Academic ([Wikidata](https://www.wikidata.org/entity/Q39631))
- **Category:** Academics
- **Born:** 1985
- **Status:** living
- **Gender:** male

## Bio

Born April 16, 1985, Sébastien Bubeck built his early career at the edges of theory and practice: Berkeley, then Princeton as assistant professor, working the problems of online learning and optimization. Microsoft Research Redmond brought him in to lead the Machine Learning Foundations group, and he rose to vice president of applied research and distinguished scientist. The work shifted as the models grew larger - he trained the theoretical lens on deep neural networks, particularly transformers, asking questions about why they work at all. In 2024 he moved to OpenAI, carrying that same scrutiny forward.

## Fame
- **Fame score:** 55.7
- **Momentum:** 21.5
- **Category rank:** #200
- **Total followers across platforms:** 24,500

## Platforms
- **X**: https://x.com/SebastienBubeck (@SebastienBubeck)
- **WEBSITE**: http://sbubeck.com/
- **YOUTUBE**: https://www.youtube.com/channel/UC-UC-nE8B33UGnC-NRaSfug (@@sebastienbubeck, 24,500 followers)

## Sources
- [Wikidata](https://www.wikidata.org/wiki/Q110407022)
- [Sébastien Bubeck](https://en.wikipedia.org/wiki/S%C3%A9bastien_Bubeck)

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