Yifan Sun

Optimization and algorithms

My research centers on the design and analysis of optimization algorithms, particularly those arising in large-scale machine learning and scientific computing. I study how structure — such as sparsity, curvature, or decomposability — can be exploited to design faster, more stable, or more interpretable methods. This includes both first-order methods and quasi-Newton techniques, with applications ranging from convex programming to nonconvex deep models.

Optimization in machine learning

I’m also interested in how optimization and algorithm plays out in machine learning, through model training, generalization, task transfer, etc. My work explores how theoretical insights — particularly from linear algebra and geometry — can lead to better understanding of deep models, regularization, and representation learning.