Zhishuai Guo (郭志帅) Assistant Professor Department of Computer Science Northern Illinois University DeKalb, IL zguo [at] niu.edu

I am an Assistant Professor at Northern Illinois University. I got my Ph.D. degree at Texas A&M University, advised by Prof. Tianbao Yang. Here is my [Google Scholar Profile].

Research Interest

• Federated Learning, Machine Learning, Optimization, Computer Vision.

Publications (Full List at [Google Scholar])

• Communication-Efficient Federated AUC Maximization with Cyclic Client Participation [Paper]
  Umesh Vangapally, Wenhan Wu, Chen Chen, Zhishuai Guo
  Transactions on Machine Learning Research, 2026.
  

• UniSTFormer: Unified Spatio-Temporal Lightweight Transformer for Efficient Skeleton-Based Action Recognition [Paper]
  Wenhan Wu, Zhishuai Guo, Chen Chen, Aidong Lu
  International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2026.
  

• Frequency-Semantic Enhanced Variational Autoencoder for Zero-Shot Skeleton-based Action Recognition [Paper]
  Wenhan Wu, Zhishuai Guo, Chen Chen, Hongfei Xue, Aidong Lu
  In International Conference on Computer Vision (ICCV), 2025.
  

• BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text [Paper]
  Ibrahim Al Azher, Miftahul Jannat Mokarrama, Zhishuai Guo, Sagnik Ray Choudhury, Hamed Alhoori
  In Findings of the Association for Computational Linguistics: EMNLP, 2025.
  

• Communication-Efficient Federated Group Distributionally Robust Optimization [Paper]
  Zhishuai Guo, Tianbao Yang
  arXiv preprint arXiv:2410.06369
  In Advances in Neural Information Processing Systems (NeurIPS), 2024.
  

• Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator [Paper]
  Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
  arXiv preprint arXiv:2104.14840
  Machine Learning.
  

• FeDXL: Provable Federated Learning for Deep X-Risk Optimization [Paper]
  Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang
  In International Conference on Machine Learning (ICML), 2023
  

• Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization [Paper]
  Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang
  International Conference on Machine Learning (ICML), 2023
  

• Fast Objective and Duality Gap Convergence for Nonconvex-Strongly-Concave Min-Max Problems with PL Condition [Paper]
  Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang
  Journal of Machine Learning Research.
  

• Compositional Training for End-to-End Deep AUC Maximization [Paper]
  Zhuoning Yuan, Zhishuai Guo, Nitesh Chawla, Tianbao Yang
  In International Conference on Learning Representations (ICLR), 2022 (Spotlight)
  

• A Novel Convergence Analysis for Algorithms of the Adam Family [Short Version] [Full Version]
  Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
  In NeurIPS Workshop on Optimization for Machine Learning (OPT), 2021
  

• An Online Method for Deep Distributionally Robust Optimization [Paper]
  Qi Qi*, Zhishuai Guo*, Yi Xu, Rong Jin, Tianbao Yang (*Equal Contribution)
  In Advances in Neural Information Processing Systems (NeurIPS), 2021
  

• Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity [Paper] [Code]
  Zhuoning Yuan*, Zhishuai Guo*, Yi Xu, Yiming Ying, Tianbao Yang (*Equal Contribution)
  In International Conference on Machine Learning (ICML), 2021
  

• Accelerating Deep Learning with Millions of Classes [Paper]
  Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang
  In European Conference on Computer Vision (ECCV), 2020
  

• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [Paper] [Code]
  Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
  In International Conference on Machine Learning (ICML), 2020
  

• A New Local Density for Density Peak Clustering [Paper]
  Zhishuai Guo, Tianyi Huang, Zhiling Cai, William Zhu
  In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018
  

Preprints

• Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization [Paper]
  Zhishuai Guo, Quanqi Hu, Lijun Zhang, Tianbao Yang
  arXiv preprint arXiv:2105.02266
  

Education

• Ph.D. in Computer Science, Texas A&M University
  

• M.S. in Computer Science, The University of Iowa
  

• B.S. in Computer Science, University of Electronic Science and Technology of China
  

Teaching

• CSCI:644: Applied Machine Learning, Fall 2025.
  

• CSCI:340: Data Structure and Algorithm Analysis, Spring 2026, Spring 2025.
  

• CSCI:644: Applied Machine Learning, Fall 2024.
  

Industry Experience

• Meta, Software Engineer Intern, June 2022 - Aug 2022.
  

• Amazon, Applied Scientist Intern, June 2021 - Aug 2021.