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Hongyang Ryan Zhang Address: 177 Huntington Ave 2211, Boston, MA 02115 Email: ho.zhang@northeastern.edu |
I am an assistant professor at Northeastern University in the Khoury College of Computer Sciences. I work on a range of topics at the intersection of machine learning, optimization algorithms, and statistical learning.
On the machine learning side, we have been working toward understanding the foundation of foundation models, for instance by designing new algorithms for multitask learning, supervised fine-tuning, and in-context learning. I am particularly interested in understanding how neural networks learn to extract information from data, and how this knowledge transfers to downstream tasks. To this end, we are developing a Hessian-based computational framework where we probe the second-order information such as the spectral statistics of the loss Hessian, and apply this framework to various downstream settings.
On the theory side, we have been looking into sampling complexities for learning graph neural networks and geometric data, low-rank matrix completion given little data, and multi-objective reinforcement learning.
Other topics I have been thinking about include algorithmic mechanism design for policy learning, formulating regularization in LLM reasoning, and time-series graph data in the context of road networks from a safety perspective.
I received my Ph.D. in Computer Science from Stanford University and my B.Eng. in Computer Science from Shanghai Jiao Tong University. Subsequently, I spent a year as a postdoc within the Statistics and Data Science department at the Wharton School of the University of Pennsylvania.
I enjoy working on technically challenging problems, while striving for broader impacts by creating new knowledge that will benefit the society, as well as fostering the next generation of engineers and researchers. I support accessible and reproducible research. If you are a student at Northeastern interested in working with me and my group, please feel free to email me.
Recent updates
A satellite image dataset for traffic accident prediction and causal analysis.
A new paper on training neural networks to understand algorithmic reasoning!
A new algorithm for multi-objective and meta reinforcement learning!
Talk slides about recent work on in-context learning and a Hessian view of grokking.
Talk slides about our work on a Hessian view of learning.
New paper on a linear-time data selection algorithm for in-context learning.
An updated paper on transfer learning random matrices accepted at JMLR! We analyze a (classical) hard parameter sharing estimator and find that simply rebalancing the data leads to minimax optimal rates.
New manucript on an ensemble method for fine-tuning LLMs, built on top of LoRA.
A list of older logs.