About

I am a Ph.D. student in computer science (CS) at the University of Illinois Urbana–Champaign (UIUC), fortunately advised by Professor Hanghang Tong. Prior to this, I received an M.S. degree in CS at UIUC and a B.E. degree in operations research and a minor in statistics at Tsinghua University. My research interests lie broadly in machine learning (ML), with a current emphasis on the following topics:

• Optimization: zeroth-order optimization, combinatorial optimization;
• Graph ML: graph diffusion, graph optimal transport;
• Trustworthy ML: robustness, fairness, imbalance;
• Applications: time series analysis, recommender systems.

Education

• University of Illinois Urbana–Champaign | Siebel School of Computing and Data Science
  M.S. & Ph.D. in Computer Science, Fall 2022 – Spring 2027 (Expected)
  Advisor: Professor Hanghang Tong
• Tsinghua University | Department of Industrial Engineering
  B.E. in Operations Research & Minor in Statistics, Fall 2018 – Spring 2022

Experience

• Qualcomm AI Research | Research Intern
  Mixed-precision quantization of large vision models, June – August 2024
  Mentors: Burak Bartan, Weiliang Will Zeng
• Qualcomm AI Research | Research Intern
  Code generation with large language models, May – August 2023
  Mentor: Weiliang Will Zeng

Publications

Conference Papers

• [ICML 2024] Gradient compressed sensing: A query-efficient gradient estimator for high-dimensional zeroth-order optimization
  Ruizhong Qiu, Hanghang Tong
  Proceedings of the 41st International Conference on Machine Learning, 2024
  [Code] | [Paper] | [Poster]
• [ICML 2024] Graph mixup on approximate Gromov–Wasserstein geodesics
  Zhichen Zeng, Ruizhong Qiu, Zhe Xu, Zhining Liu, Yuchen Yan, Tianxin Wei, Lei Ying, Jingrui He, Hanghang Tong
  Proceedings of the 41st International Conference on Machine Learning, 2024
  [Code] | [Paper]
• [ICML 2024] Class-imbalanced graph learning without class rebalancing
  Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong
  Proceedings of the 41st International Conference on Machine Learning, 2024
  [Code] | [Paper]
• [NeurIPS 2024] Discrete-state continuous-time diffusion for graph generation
  Zhe Xu, Ruizhong Qiu, Yuzhong Chen, Huiyuan Chen, Xiran Fan, Menghai Pan, Zhichen Zeng, Mahashweta Das, Hanghang Tong
  Advances in Neural Information Processing Systems 37, 2024
• [NeurIPS 2024 Spotlight] BackTime: Backdoor attacks on multivariate time series forecasting
  Xiao Lin, Zhining Liu, Dongqi Fu, Ruizhong Qiu, Hanghang Tong
  Advances in Neural Information Processing Systems 37, 2024
• [NeurIPS 2022] DIMES: A differentiable meta solver for combinatorial optimization problems
  Ruizhong Qiu*, Zhiqing Sun*, Yiming Yang
  Advances in Neural Information Processing Systems 35, 2022
  [Code] | [Paper] | [Slides] | [Poster]
• [VLDB 2025] TUCKET: A tensor time series data structure for efficient and accurate factor analysis over time ranges
  Ruizhong Qiu*, Jun-Gi Jang*, Xiao Lin, Lihui Liu, Hanghang Tong
  Proceedings of the VLDB Endowment 17(13), 2024
  [Code]
• [KDD 2025] Generalizable recommender system during temporal popularity distribution shifts
  Hyunsik Yoo, Ruizhong Qiu, Charlie Xu, Fei Wang, Hanghang Tong
  Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025
• [KDD 2024] AIM: Attributing, interpreting, mitigating data-encoded unfairness
  Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Yada Zhu, Hendrik Hamann, Hanghang Tong
  Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
  [Code] | [Paper]
• [KDD 2023] Reconstructing graph diffusion history from a single snapshot
  Ruizhong Qiu, Dingsu Wang, Lei Ying, H. Vincent Poor, Yifang Zhang, Hanghang Tong
  Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
  [Code] | [Paper] | [Slides] | [Poster]
• [KDD 2023] Networked time series imputation via position-aware graph enhanced variational autoencoders
  Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew J. Margenot, Hanghang Tong
  Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
  [Paper] | [Extended]
• [WWW 2024] Ensuring user-side fairness in dynamic recommender systems
  Hyunsik Yoo, Zhichen Zeng, Jian Kang, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, Hanghang Tong
  Proceedings of the ACM Web Conference 2024, 2024
  [Code] | [Paper] | [Appendix]
• [CIKM 2024] On the sensitivity of individual fairness: Measures and robust algorithms
  Xinyu He, Jian Kang, Ruizhong Qiu, Fei Wang, Jose Sepulveda, Hanghang Tong
  Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024
• [FAccT 2024] Group fairness via group consensus
  Eunice Chan*, Zhining Liu*, Ruizhong Qiu, Yuheng Zhang, Ross Maciejewski, Hanghang Tong
  Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024
  [Paper]

Preprints

• Ask, and it shall be given: Turing completeness of prompting
  Ruizhong Qiu, Zhe Xu, Wenxuan Bao, Hanghang Tong
  arXiv preprint, 2024
• WAPITI: A watermark for finetuned open-source LLMs
  Lingjie Chen*, Ruizhong Qiu*, Siyu Yuan, Zhining Liu, Tianxin Wei, Hyunsik Yoo, Zhichen Zeng, Deqing Yang, Hanghang Tong
  arXiv preprint, 2024
• Fair anomaly detection for imbalanced groups
  Ziwei Wu, Lecheng Zheng, Yuancheng Yu, Ruizhong Qiu, John Birge, Jingrui He
  arXiv preprint, 2024
• How efficient is LLM-generated code? A rigorous & high-standard benchmark
  Ruizhong Qiu, Weiliang Will Zeng, Hanghang Tong, James Ezick, Christopher Lott
  arXiv preprint, 2024
  [GitHub] | [HuggingFace] | [PyPI]

* Equal contribution.

Honors

• Siebel Scholar, Class of 2024 (83 recipients in U.S.A., China, France, Italy, and Japan)
• Conference Presentation Award, UIUC Graduate College, Fall 2022
• NeurIPS Scholar Award, NeurIPS 2022
• Gold Medal (rank 6/353), ACM International Collegiate Programming Contest (ICPC), Asia QingDao Regional, 2018
• Silver Medal, National Olympiad in Informatics, China, 2017

Invited Talk

• Minisymposium on New Frontier of Graph Machine Learning, SIAM Conference on Mathematics of Data Science (MDS), 2024

Academic Service

• Reviewer (2 papers), AISTATS 2025
• Program Committee Member (1 paper), AAAI 2025
• Reviewer (1 paper), ICLR 2024
• Reviewer (6 papers), NeurIPS 2024
• Program Committee Member (2 papers), DSAA 2024
• Program Committee Member & Reviewer (7 papers), CIKM 2024