Assistant Professor of Computer Science Khoury College of Computer Sciences Northeastern University Email: hongyang90@gmail.com, or ho.zhang@northeastern.edu Address: 177 Huntington Ave, Room 2211 [Google Scholar]  [Resume]

Hi! I am an assistant professor of computer science at Northeastern University since Fall 2020. I am interested in algorithmic and data-centric aspects of machine learning and network analysis.

• On the machine learning side, I'm interested in methodological and theoretical questions related to ML algorithms and generalization, including:

  • Multitask and transfer learning, data augmentation, and self-supervised learning (i.e., contrastive learning) for learning from limited data.

  • Robustness (and bias) of ML algorithms under distribution shifts.

• On the network side, I'm interested in the design of efficient algorithms for network centrality and epidemics on social networks and mobility networks.

I came to Northeastern after a brief postdoc study at UPenn. I received my Ph.D. from Stanford, advised by Ashish Goel and Greg Valiant. I also collaborated with Tengyu Ma and Chris Ré's groups at Stanford. I received a B.Eng. from Shanghai Jiao Tong University (the ACM class).

Research

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
  Working paper, 2021
  

Publications

• Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
  Haotian Ju, Dongyue Li, and H. R. Zhang
  Interntional Conference on Machine Learning (ICML) 2022

• Incentive Ratio: A Game Theoretical Analysis of Market Equilibria
  Ning Chen, Xiaotie Deng, Bo Tang, H. R. Zhang, and Jie Zhang
  Information and Computation, 2022

• Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations
  Michael Zhang, Nimit Sohoni, H. R. Zhang, Chelsea Finn, and Christopher Ré
  International Conference on Machine Learning (ICML) 2022. Long presentation.
  Also presented at NeurIPS Workshop on Distribution Shifts (2021) and ICML Workshop on Uncertainty and Robustness in Deep Learning (2021)

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

• 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] [code]

• Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
  Yuanzhi Li, Tengyu Ma, and H. R. Zhang*
  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]

• 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
  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
  [code]

• Incentives for Strategic Behavior in Fisher Market Games
  Ning Chen, Xiaotie Deng, Bo Tang, and H. R. Zhang*
  AAAI 2016

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

• Connectivity in Random Forests and Credit Networks
  Ashish Goel, Sanjeev Khanna, Sharath Raghvendra, and H. R. Zhang*
  Symposium on Discrete Algorithms (SODA) 2015

• Computing the Nucleolus of Matching, Cover and Clique Games
  Ning Chen, Pinyan Lu, and H. R. Zhang*
  AAAI 2012. oral presentation

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

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

Remark: Some of my papers, marked by an asterisk, use an alphabetical ordering of author names. If you find my work interesting, you might also be interested in checking out the following 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.

Students

Ph.D. students

• Dongyue Li (Fall 2021)

• Haoyu He (incoming, Fall 2022)

Master's students

• Haotian Ju (Spring 2021)
  

• Shreya Singh (Spring 2022)

Alumni

• Virender Singh (MS in CS, Fall 2021)

Prospective students: I'm always looking for students. If you are already at Northeastern, please feel free to get in touch. We have projects that could use your help: Check out our recent projects at GitHub. Feel free to send me your CV and describe how you would like to contribute. If you did not receive a reponse from me in one week, feel free to send it again (I apologize in advance). Thanks! You could also find other faculty who work on related topics at Northeastern in the Statstical Machine Learning group and the Theory group.

Teaching

• DS 5220, Supervised Machine Learning and Learning Theory, Fall 2021, Fall 2022.

• CS 7140, Advanced Machine Learning, Spring 2021, Spring 2022.

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

Education/Experience

• 2020: postdoc at UPenn.

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

• 2013: Research assistant at Nanyang Technological University (advisor: Ning Chen).

• 2012: B.Eng. from Shanghai Jiao Tong University.