Presenter: Georgios Liargkovas, Columbia University
Date: 09 July 2025

For decades, OS tuning has relied on static heuristics that cannot adapt to dynamic, complex workloads. While machine learning offered a path forward, traditional models like Bayesian Optimization and Reinforcement Learning introduced their own challenges: a "semantic gap" preventing true contextual understanding, brittle reward engineering, and inefficient exploration unfit for live systems. This talk argues that Large Language Models (LLMs) represent the next leap forward. We present preliminary, promising results from an LLM-powered autonomous agent that leverages reasoning and pre-trained knowledge to overcome these limitations.

We will conclude by discussing future research directions for these emerging autonomous systems.

Biography

Georgios Liargkovas is a PhD student at Columbia University advised by Kostis Kaffes. His research focuses on OS scheduling and AI/ML for OS Optimization. He holds a BS in Management Science and Technology from Athens University of Economics and Business, where he conducted empirical software engineering research at BALab advised by Diomidis Spinellis.