Hongyang (Ryan) Zhang Address: |
Hi, I am an assistant professor of computer science at Northeastern University, Boston, working at the intersection of machine learning, learning theory, design and analysis of algorithms, social and information networks. Some of the topics I work on are:
Matrix and tensor factorization, implicit regularization of SGD.
PAC-Bayes bounds, Hessian-based generalization.
Multitask learning, fine-tuning, robustness, foundation models.
Social, information, and transportation networks.
Graph neural networks and their applications to sciences and engineering.
I received a Ph.D. in computer science from Stanford, where I worked within the theoretical computer science and the statistical machine learning groups. I was a postdoc at UPenn for a brief stint. More information about me can be found in my cv.
I enjoy working on technically challenging problems, and strive for broader impacts by creating new knowledge and promoting education. I support accessible and reproducible research. See our experiment codes on GitHub. Here is a list of my recent activities.
See below for some of my representative papers (see also Google Scholar)
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion, Artificial Intelligence and Statistics (AISTATS) 2023
H. Ju, D. Li, A. Sharma, and H. R. Zhang
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees, International Conference on Machine Learning (ICML) 2022
H. Ju, D. Li, and H. R. Zhang
Understanding and Improving Information Transfer in Multi-Task Learning, International Conference on Learning Representations (ICLR) 2020
S. Wu*, H. R. Zhang*, and C. Ré
Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations, Conference on Learning Theory (COLT) 2018
Y. Li*, T. Ma*, and H. Zhang*
Approximate Personalized PageRank on Dynamic Graphs, SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2016
H. Zhang, P. Lofgren, and A. Goel
Connectivity in Random Forests and Credit Networks, Symposium on Discrete Algorithms (SODA) 2015
A. Goel*, S. Khanna*, S. Raghvendra*, and H. Zhang*
Incentive Ratios of Fisher Markets, International Conference on Automata, Languagees, and Programming (ICALP) 2012
N. Chen*, X. Deng*, H. Zhang*, and J. Zhang*
DS 5220, Supervised Machine Learning and Learning Theory, Fall 2021, Fall 2022, Fall 2024.
CS 6140, Machine Learning, Fall 2023.
CS 7140, Advanced Machine Learning, Spring 2021, Spring 2022, Spring 2023. This class supercedes Algorithmic and Statistical Aspects of Deep Learning (CS 7180, Fall 2020). It covers topics spanning statistical learning theory, algorithmic aspects of machine learning, and theory of deep learning.
DS 4400, Machine Learning and Data Mining I, Spring 2023.
Here is a a deck of slides that presentes our recent and ongoing work where we develop neural networks to simultaneously solve many tasks.
Current Ph.D. students and postdocs
Dongyue Li, CS PhD
Haotian Ju, MS in Data Analytics, CS PhD starting Fall 2024
Mahdi Haghifam, postdoc. Joint Mentor: Jonathan Ullman
Current MS and undergraduate students
Abhinav Nippani, MS in CS
Kailai Chen, B.S. in Maths and Statistics
Yangnan Lin, B.Eng. in CS
Prospective students: I am actively looking for students to join us. If you have ideas, I would love to chat. We host visiting students and scholars, and we are open to working with students who are already on campus. You may take a look at my recent papers and projects first before reaching out to me. I am particularly interested in students who are self-motivated, and have a strong background in mathematics and/or programming.
You can email me at hongyang90@gmail.com.
Program committee: COLT (2024), ICML (2019-2023; meta-reviewer, 2024), AISTATS (2021-2022; meta-reviewer, 2023/24), ALT (meta-reviewer, 2024), ICLR (2021-2024), NeurIPS (2019-2023), ICLR (2021-2024), KDD 2023, WWW 2022, WSDM (2022-2024).
Conference organization: Informs 2023-2024 (session chair).
October 2019 to July 2020: Postdoctoral researcher at The University of Pennsylvania.
September 2013 to September 2019: Ph.D. from Stanford (advisors: Ashish Goel and Greg Valiant).
July 2012 to July 2013: Research assistant at Nanyang Technological University (advisor: Ning Chen, mentored by Xiaotie Deng).
September 2008 to September 2012: B.Eng. from Shanghai Jiao Tong University (advisors: Ning Chen and Pinyan Lu, part of the ACM class founded by Prof. Yong Yu).
I grew up in China. My Chinese name is written as 张泓洋.