I am a third year Statistics PhD student at the University of Chicago. My broad research interest is to understand the empirical phenomena in modern machine learning — where modeling, high-dimensionality, and optimization are intertwined. The goal is to use these insights to derive principled machine learning algorithms. I am currently working on identifying the favorable properties of neural networks that lead to good generalization. I am very fortunate to work with professor Tengyuan Liang. I enjoy simple statements.
The blog posts are meant to summarize some interesting papers I have read, and to share some research thoughts. I hope to bring attention to new and old ideas in the literature of machine learning.