Assistant Professor Khoury College of Computer Sciences Northeastern University, Boston Email: ho.zhang@northeastern.edu Address: 177 Huntington Ave, Room 2211 (note) DBLP, Google Scholar LinkedIn, Twitter

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 and engineers.

I am a member of the algorithms and theory group within Northeastern, and a core member of the Institute for Experiential AI.

I support accessible and reproducible research. You can find some of our experiment codes on GitHub. Here is a list of my recent activities.

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

Remarks

• List of papers sorted by year

• Research summary (currently under construction)

Advising/Teaching

• Dongyue (Oliver) Li, Ph.D., since 2021

• Mahdi Haghifam, Postdoctoral Researcher (jointly mentored by Jonathan Ullman), Fall 2023

• Abhinav Nippani, MS in CS, Spring 2023

• Jinhong Yu, MS in AI, Spring 2023

• Kailai Chen, Undergraduate in Maths and Stats, visiting from Xi'an Jiao Tong University, Fall 2023

• Allen Ye, Undergraduate in CS, Fall 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

• Haotian Ju, MS in Data Analytics Engineering, 2022; Currently working at a Quantitative Research firm, will return as a Ph.D. student in Fall 2024

Prospective students: I am always looking for students to join our research team. If you have ideas, I'd love to chat. You can take a look at my recent papers and projects. I'm particularly looking for students who are self-motivated, and have a strong background in learning theory, algorithms, and programmining.

Classes

• CS 7140, Advanced Machine Learning, Spring 2021, 2022, 2023. This class supercedes CS 7180, Special Topics in AI: Algorithmic and Statistical Aspects of Deep Learning, which I taught in Fall 2020.

• CS 6140, Machine Learning, Fall 2023.

• DS 4400, Machine Learning and Data Mining I, Spring 2023.

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

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, also 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 mentored by Yong Yu).

Misc

My name written in Chinese characters: 张泓洋