Machine Learning Summer School Brings Global Researchers to Columbia
Columbia University hosted the Machine Learning Summer School (MLSS) 2026 from June 15 to 26, drawing more than 200 PhD students, faculty, and industry practitioners from around the world. It was the first time the program had been held in New York City. MLSS was organized by Columbia Engineering's IEOR department alongside Bloomberg, the Columbia Data Science Institute, NYU's Center for Data Science, Cornell Tech, and Stony Brook University. Participants engaged in lectures, tutorials, and hands-on labs covering topics from reinforcement learning to AI safety.
Several themes resonated strongly with attendees. Causal AI, which focuses on teaching systems to understand cause and effect rather than just patterns, emerged as one of the most exciting and underexplored areas in the field. Interpretability was another major focus, with participants stressing that AI systems used in healthcare, robotics, and critical infrastructure must be explainable. Sessions on agentic AI offered practical insights into how major investment banks like Morgan Stanley are deploying them in real-world settings.
The program emphasized that meaningful progress in AI demands interdisciplinary collaboration. Attendees came from backgrounds spanning psychology, neuroscience, and engineering, and many noted that the exchange of ideas across fields was one of the most valuable parts of the experience. By the end of the two weeks, participants walked away not just with new technical skills but with a broader understanding of where AI research is heading and the responsibilities that come with it.
