Machine Learning Summer School (MLSS 2026)
Machine Learning Summer School (MLSS 2026), Columbia University, New York, USA (In-person)
Joint summer school program organised by Columbia University, Cornell University, New York University, Center for Data Science, Stony Brook University, and Bloomberg.
Topics Covered
- Reinforcement Learning
- Natural Language Processing (NLP)
- Causal Artificial Intelligence
- Causal Discovery and Learning
- Causal Machine Learning Foundations
- Causal Machine Learning for Policy and Science
- Causal Meta-Learning
- Agentic AI Foundations
- Agentic AI
- Agentic AI Evaluation
- Agentic AI for Scientific Discovery
- Agentic AI for Automated Research
- AI for Healthcare
- AI in Quantitative Finance
- AI for Science
- Foundation and Frontier Models
- Large Language Models (LLMs)
- Time Series Analysis
- Time Series Modelling
- Probabilistic Modelling
- Generalization in Machine Learning
- Neurosymbolic AI
- Diffusion Models
- Mechanistic Interpretability
- Systems and Efficiency for AI
- Temporal Stability in AI Systems
- AI Safety and Governance
- AI Safety for Autonomous Agents
- Technology Ethics in the Age of AI
- Pitfalls of AI Scientists
Participated in the Machine Learning Summer School (MLSS 2026) hosted at Columbia University, receiving advanced training from leading researchers in modern artificial intelligence, including causal AI, agentic AI, foundation models, AI for healthcare, mechanistic interpretability, AI safety, reinforcement learning, and scientific machine learning.
Organising Institutions
