Assistant Professor of Computer Science Khoury College of Computer Sciences Northeastern University ho.zhang@northeastern.edu (work) hongyang@cs.stanford.edu (personal) Address: 177 Huntington Ave, Room 2211 (note). [G]  [DBLP]  [CV]

Hi! I am an assistant professor of computer science at Northeastern University since Fall 2020. My research interests lie at the intersection of learning theory, machine learning, and networks, with a particular emphasis on the design and analysis of algorithms, and the generalization properties of deep neural networks. I received my Ph.D. from Stanford, advised by Ashish Goel and Greg Valiant. Meanwhile, I had the great fortune working with the TCS and ML groups at Stanford. Before moving to Boston, I spent ten months as a postdoc at UPenn's statistics department. I received my B.Eng. from Shanghai Jiao Tong University.

My current research focuses on methodological and theoretical questions in machine learning and graphs, including:

• Generalization: Generalization and information transfer in deep and pretrained neural networks.

• Weak Supervision: Robust methods for using noisy information in multitask learning and in data augmentation.

• Graph Algorithms and Learning: PageRank and spectral graph theory, shortest path, and graph neural networks.

Research

2022

• Optimal Intervention on Weighted Networks via Edge Centrality
  Dongyue Li, Tina Eliassi-Rad, and H. R. Zhang
  International workshop on Epidemiology meets Data Mining and Knowledge discovery (epiDAMIK) 2022

• 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
  [ICML Updatable ML Workshop 2022]
  [code]

• 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
  [NeurIPS DistShift Workshop 2021]
  [ICML UDL Workshop 2021]

• 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

2021

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

• 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
  

• 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
  

2020

• 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
  [code]
  [blog: Automating the Art of Data Augmentation – Part III Theory]
  

• 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
  [video]
  [blog: When Multi-Task Learning Works – And When It Doesn’t]
  

Prior to 2019

• Algorithms and Generalization for Large-scale Matrices and Tensors
  Thesis Ph.D. Stanford University 2019
  H. R. Zhang

• 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
  [code]

• 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.

Students

I work with an amazing group of students including: Dongyue Li (Ph.D., starting 2021), Haoyu He (Ph.D., starting 2022), Haotian Ju (MS in Data Analytics, since 2021), Shreya Singh (MS in CS, since 2022), and Jack Wilkins (Undergraduate in CS, since 2022).

Graduated students: Virender Singh (MS 2021; First employment: SDE at Salesforce)

Prospective students interested in pursuing theoretical and algorithmic aspects of machine learning are welcome to contact me. Take a look at my recent papers and recent projects at GitHub. Undergraduate students interested in pursuing research are also encouraged to contact me. If you did not receive a reponse from me in one week, please send it again. Thanks!

Recent activities

• November 2022 (talk): Excited to receive an invitation as part of AAAI’23 New Faculty Highlights!

• October 2022 (talk): Talked about transfer learning and random matrix theory at INFORMS.

• September 2022 (service): Excited to be an area chair of AISTATS’23!

• August 2022 (service): PC member of WSDM’23 and AAAI’23.

• August 2022 (paper): presented a new algorithm about reducing epidemic spread at the epiDAMIK workshop at KDD’22.

• May 2022 (paper): Excited about two recent works that will be presented at ICML’22 (thanks to the anonymous referees for feedback!):

  • One paper with Michael Zhang et al. about a contrastive learning approach for improving worst-group performance.

  • Another paper with my students about Hessian based generalization for fine-tuned models.

• May 2022 (talk): gave a talk about understanding and improving generalization in multitask and transfer learning at the One World ML Seminar.

• A list of old updates

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, and also Xiaotie Deng).

• 2012: B.Eng. from Shanghai Jiao Tong University (advisors: Ning Chen and Pinyan Lu, part of the ACM class advised by Yong Yu).