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Assistant Professor of Computer Science Email: ho.zhang@northeastern.edu |
Hi! I am an assistant professor of computer science at Northeastern University, Boston, working at the intersection of machine learning (theory and methods), algorithms, and social data (e.g., social networks). I received my Ph.D. in computer science from Stanford, and worked with the theoretical computer science and machine learning groups. I was a postdoc at University of Pennsylvania for a brief stint. You can find complete information about me in my CV. I enjoy getting deep into technically-challenging questions, while striving for broader impact to our society through advancing knowledge and educating the next-generation of researchers.
Research/Publications
Selected papers
Identification of Negative Transfers in Multitask Learning Using Surrogate Models
Dongyue Li, Huy L. Nguyen, and H. R. Zhang
Transactions on Machine Learning Research (TMLR) 2023. Featured Certification
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
Haotian Ju, Dongyue Li, Aneesh Sharma, and H. R. Zhang
Artificial Intelligence and Statistics (AISTATS) 2023
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
Haotian Ju, Dongyue Li, and H. R. Zhang
International Conference on Machine Learning (ICML) 2022
On the Generalization Effects of Linear Transformations in Data Augmentation
Sen Wu*, H. R. Zhang*, Gregory Valiant, and Christopher Ré
International Conference on Machine Learning (ICML) 2020
Understanding and Improving Information Transfer in Multi-Task Learning
Sen Wu*, H. R. Zhang*, and Christopher Ré
International Conference on Learning Representations (ICLR) 2020
Recovery Guarantees for Quadratic Tensors with Sparse Observations
H. R. Zhang, Vatsal Sharan, Moses Charikar, and Yingyu Liang
Artificial Intelligence and Statistics (AISTATS) 2019
Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations
Yuanzhi Li, Tengyu Ma, and H. R. Zhang*
Conference on Learning Theory (COLT) 2018. Best Paper Award
Approximate Personalized PageRank on Dynamic Graphs
H. R. Zhang, Peter Lofgren, and Ashish Goel
KDD 2016. Oral presentation
Connectivity in Random Forests and Credit Networks
Ashish Goel, Sanjeev Khanna, Sharath Raghvendra, and H. R. Zhang*
Symposium on Discrete Algorithms (SODA) 2015
Notes
I support accessible and reproducible research. Most of our experiment codes are available on GitHub.
I am part of the theory group within Northeastern.
Here is a list of my recent activities if you are curious about what I am up to these days.
Advising/Teaching
Dongyue (Oliver) Li, Ph.D., since 2021
Haotian Ju, MS in Data Analytics Engineering, 2022; Ph.D., starting 2023
Mahdi Haghifam, Khoury Postdoctoral Fellow (jointly working with Jonathan Ullman and me), Fall 2023
Abhinav Nippani, MS in CS, since 2023
Jinhong Yu, MS in AI, since 2023
Allen Ye, Undergraduate in CS, since 2023
Debankita Basu, MS in Data Science, Fall 2023
MS graduates
Virender Singh, MS in CS, 2021; Data Scientist at Salesforce
Minghao Liu, MS in CS, 2022; SDE at Palantir
Shreya Singh, MS in CS, 2022; Data Scientist at Credit One Bank
Prospective students: I am always looking for students to join us. If you have ideas, I'd love to chat. You can take a look at my recent papers and projects. Undergraduate students interested in pursuing research are also encouraged to contact me. Students from underrepresented groups are especially encouraged to apply to our CS Ph.D. program and will be eligible for one year of fellowship. The ideal student needs to be self-motivated, and have a strong background in algorithms and/or programming. If you are interested in working with my students and me as an RA, you can fill out this form, so that we are aware of your interest. We'll be in touch via email if we see a fit with our projects.
Classes
CS 6140, Machine Learning, Fall 2023.
CS 7140, Advanced Machine Learning, Spring 2021, Spring 2022, Spring 2023.
DS 4400, Machine Learning and Data Mining I, Spring 2023.
DS 5220, Supervised Machine Learning and Learning Theory, Fall 2021, Fall 2022.
CS 7180, Special Topics in AI: Algorithmic and Statistical Aspects of Deep Learning, Fall 2020.
Education/Experience
October 2019 to July 2020: Postdoc at 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, and also 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 advised by Yong Yu).