2026-04-14 –, Merck Plenary (Spectrum) [1st Floor]
Python has been at the center of my work in machine learning and AI for more than a decade. It is where I start from scratch, experiment with ideas, and build systems that help me understand how large language models really work.
In this keynote, we will explore how Python enables this entire journey, from defining model architectures and training loops to scaling data and computation across devices. I will also reflect on how Python continues to support both the large models of today and the evolving systems of tomorrow, even as new backends take over the heavy lifting.
Python has been at the center of my work in machine learning and AI for more than a decade. It is where I start from scratch, experiment with ideas, and build systems that help me understand how large language models really work.
In this keynote, I will look at what it means to build and study LLMs in Python today. Starting from small, from-scratch implementations, I will show how Python and PyTorch help us understand modern model architectures, compare new designs against reference code, and learn details that papers often leave out. I will then connect those implementation lessons to current LLM trends, especially the push to reduce inference costs and KV-cache pressure as reasoning models and agentic workflows need longer contexts. At the end, I will also share a practical roadmap of libraries, open projects, and learning resources for going from first principles to real-world LLM development.
Sebastian is an LLM Research Engineer with over a decade of experience in artificial intelligence. His work bridges academia and industry, including roles as a senior engineer at Lightning AI and a statistics professor at the University of Wisconsin–Madison.
He is also the author of Build a Large Language Model (From Scratch).
His expertise lies in LLM research and the development of high-performance AI systems, with a strong focus on practical, code-driven implementations.