Advances in machine learning are reshaping the foundations of science and technology. Despite their empirical success, a rigorous theoretical understanding of when and why machine learning algorithms succeed or fail remains limited. My research aims to bridge this gap by developing theoretical frameworks that explain the underlying mechanisms of machine learning and guide the design of principled and effective algorithms. In particular, I aim to understand the limitations of existing methods and to develop theoretically grounded solutions that address these limitations. My current research focuses on language models and representation learning.
Language Models:
How to Correctly Report LLM-as-a-Judge Evaluations ICML 2026
Fine-Tuning Without Forgetting In-Context Learning ICML 2026
Representation Learning:
On the Similarities of Embeddings in Contrastive Learning ICML 2025
A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning AISTATS 2025
Analysis of Using Sigmoid Loss for Contrastive Learning AISTATS 2024
You can find all my papers here, and some memorable moments here.