Tianyi Lin

His recent work focuses on neural network optimization, preference alignment for large language models, and reinforcement learning for reasoning. He also studies how AI-mediated information aggregation shapes social learning and collective knowledge.

As artificial intelligence increasingly influences decision-making, its development raises both computational and economic challenges. Building effective AI systems requires efficient training algorithms, methods for learning from feedback, and careful treatment of competing objectives. Lin’s research combines optimization theory and statistical learning techniques to address these challenges, with applications to language model training, alignment, and reasoning. In parallel, he develops economic models to understand when AI improves collective learning and when it undermines the accuracy of beliefs. His work connects the computational foundations of AI with its broader implications for information and society.

Lin received a PhD in Electrical Engineering and Computer Science from the University of California, Berkeley, in 2023 and was a postdoctoral researcher at the Massachusetts Institute of Technology’s Laboratory for Information and Decision Systems from 2023 to 2024. He is an assistant professor in the Department of Industrial Engineering and Operations Research at Columbia University and a member of its Data Science Institute.