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Hongyang (Ryan) Zhang

Hi! I am a final year graduate student in Computer Science at Stanford University. I am fortunate to be advised by Ashish Goel and Greg Valiant. My research interests lie in machine learning and algorithms, broadly including topics such as non-convex optimization, matrix and tensor factorizations, neural networks and its theory, and social network analysis.

At Stanford I often interact with the Theory Group, StatsML Group and Social Algorithms Lab. I received my Bachelor's degree from Prof. Yong Yu's ACM Honored Class in CS at Shanghai Jiao Tong University.

The goal of my work is developing meaningful theoretical insights and algorithms for real world applications. Thus far this has led to two lines of work:

  • Understanding the generalization of non-convex methods: Non-convex methods are the method of choice for training most ML models in practice. How much data is needed to train a model that generalizes well to unseen data?

  • Algorithms for social networks and their analysis beyond the worst-case: Dealing with large-scale social network data requires methods that scale to tens of millions of users. In the meantime, real world social networks admit structural properties. How do we exploit their structures to provide better algorithms and guarantees?

Contact: Gates 481 / hongyang@cs.stanford.edu

Recent News

  • I will be joining Northeastern CS as an assistant professor in Fall 2020.