Hongyang R. Zhang Assistant Professor of Computer Science Khoury College of Computer Sciences Northeastern University ho.zhang@northeastern.edu 177 Huntington Ave, Room 2211 [Google Scholar]  [Resume]

Hi! I am an assistant professor of computer science at Northeastern University. My research interests lie in machine learning, algorithms, learning theory, social and information networks. I am a member of the Statstical Machine Learning group and the Theory group.

Machine learning has made significant advance over the past decade. In order to fulfill the promise inspired by the recent development, we need a better understanding of when and why machine learning techniques work. My recent work has tapped into the algorithmic aspects of modern machine learning techniques. One particular problem that I have been focusing on over the last few years is how to learn from limited data, since annotating unlabeled data is often expensive. To this end, we have developed a rigorous framework to think about what information transfer means in the context of multi-task learning (ICLR 2020, arXiv 2021).

From a different perspective, my work has also examined the generalization effects of data augmentation (ICML 2020). In this line of ideas, we are particularly interested in quantifying the impact of changing the data, as opposed to changing the algorithm.

Finally, I'm exploring topics related to network data analysis (KDD 2016) and robustness in machine learning. In earlier works, I've also worked on understanding the dynamics of non-convex optimization algorithms such as SGD in over-parametrized neural networks (COLT 2018).

Prospective students: I'm looking for PhD students who are strong in coding and passionate about pursuing research to join my group, starting in Fall 2022. If you are interested, feel free to send me your CV and describe your interests. Thanks!

Recent updates

• Sep 2021: Welcoming Dongyue and Virender to the group!

• Aug 2021: A new manuscript that explains positive (and negative) transfer using random matrix theory!

• Mar 2021: Received a Khoury seed grant for investigating fairness in ML (Joint w/ Huy Nguyen).

• A list of old updates

Manuscripts

• Analysis of Information Transfer from Heterogeneous Sources via Precise High-dimensional Asymptotics
  Fan Yang, H.R. Zhang, Sen Wu, Weijie Su, and Christopher Ré

Publications

(asterisk indicates alphabetical authorship or equal contribution)

• Improved Regularization and Robustness for Fine-tuning in Neural Networks
  Dongyue Li and H. R. Zhang
  Neural Information Processing Systems (NeurIPS) 2021
  

• Observational Supervision for Medical Image Classification using Gaze Data
  Khaled Saab, Sarah Hooper, Nimit Sohoni, Jupinder Parmar, Brian Pogatchnik, Sen Wu, Jared Dunnmon, H. R. Zhang, Daniel Rubin, and Christopher Ré
  Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021
  

• 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
  [blog]

• Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
  with Yuanzhi Li and Tengyu Ma
  Conference on Learning Theory (COLT) 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
  [blog] [video] [arxiv]

• Pruning based Distance Sketches with Provable Guarantees on Random Graphs
  H. R. Zhang, Huacheng Yu, and Ashish Goel
  The Web Conference (WWW) 2019 (oral presentation)

• Recovery Guarantees for Quadratic Tensors with Limited Observations
  H. R. Zhang, Vatsal Sharan, Moses Charikar, and Yingyu Liang
  International Conference on AI and Statistics (AISTATS) 2019

• Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations
  with Yuanzhi Li and Tengyu Ma
  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) [code]

• Incentives for Strategic Behavior in Fisher Market Games
  with Ning Chen, Xiaotie Deng, Bo Tang
  AAAI 2016

• A Note on Modeling Retweet Cascades on Twitter
  with Ashish Goel, Kamesh Munagala, Aneesh Sharma
  Workshop on Algorithms and Models for the Web Graph (WAW) 2015

• Connectivity in Random Forests and Credit Networks
  with Ashish Goel, Sanjeev Khanna, Sharath Raghvendra
  Symposium on Discrete Algorithms (SODA) 2015

• Computing the Nucleolus of Matching, Cover and Clique Games
  with Ning Chen and Pinyan Lu
  AAAI 2012 (oral presentation)

• Incentive Ratios of Fisher Markets
  with Ning Chen, Xiaotie Deng, Jie Zhang
  International Colloquium on Automata, Languages, and Programming (ICALP) 2012

• On Strategy-proof Allocation without Payments or Priors
  with Li Han, Chunzhi Su, Linpeng Tang
  Conference on Web and Internet Economics (WINE) 2011

Teaching

• DS 5220, Supervised Machine Learning and Learning Theory, Fall 2021

• CS 7140, Advanced Machine Learning, Spring 2021

• CS 7180, Special Topics in AI: Algorithmic and Statistical Aspects of Deep Learning, Fall 2020
  

Education

• 2020: postdoc at UPenn.

• 2019: Ph.D. from Stanford (advisors: Ashish Goel and Greg Valiant).

• 2012: BEng from Shanghai Jiao Tong University.

Blog posts

• When Multi-Task Learning Works – And When It Doesn’t
  based on joint work with Sen Wu and Chris Ré.

• Automating the Art of Data Augmentation – Part III Theory
  edited with Sharon Y. Li and Chris Ré.

Misc

My Chinese name written in Chinese characters is 张泓洋.