Participation at the Machine Learning Summer School 2026 at Columbia University

29 May 2026

Manolis participated in the Machine Learning Summer School (MLSS) at Columbia University in New York.

The two-week program brought together 200 PhD students, faculty, industry speakers, and invited practitioners for an intensive series of lectures, tutorials, discussions, and networking activities on recent developments in machine learning. MLSS was jointly overseen by a Steering Committee and a Local Organizing Committee led by Columbia University, with organizers representing Bloomberg, Columbia Engineering, Columbia’s Data Science Institute, NYU Center for Data Science, and Cornell Tech.

The summer school covered a broad range of topics in modern machine learning, including reinforcement learning for LLM post-training, mechanistic interpretability, alignment and safety, RAG and agents, time series analysis, and efficient systems for large language models.

During MLSS, Manolis also had the opportunity to present his own work to fellow participants, exchange ideas with researchers from different backgrounds, and engage in discussions on current and emerging challenges in machine learning.