Welcome to my homepage!

My name is Zhuqing (Zuki) Liu. I obtained my Ph.D. degree in the Dept. of ECE at The Ohio State University, supervised by Prof. Jia(Kevin) Liu.

I am an Assistant Professor in Department of Computer Science and Engineering at the University of North Texas.

[Recruiting PhDs & Interns]: I am actively seeking highly motivated students for Ph.D. positions and research internships. If you are interested, please email me your CV and transcripts.

Email: Zhuqing.Liu@unt.edu

Google Scholar: https://scholar.google.com/citations?user=7NbxFGYAAAAJ&hl=en

Linkedin: https://www.linkedin.com/in/zhuqing-liu-816610155/

RESEARCH INTERESTS

First-order stochastic optimization for machine learning
Communication efficiency in distributed/federated/decentralized learning
Meta Learning, Large Language Model, AI Security

Publications

Zhuqing Liu, Chaosheng Dong, Michinari Momma, Simone Shao, Shaoyuan Xu, Haibo Yang, Jia Liu “STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning” in UAI 2025. (acceptance rate: 31%)
Baolei Zhang, Haoran Xin, Jiatong Li, Dongzhe Zhang, Minghong Fang, Zhuqing Liu, Lihai Nie, and Zheli Liu. Benchmarking Poisoning Attacks against Retrieval-Augmented Generation. arXiv preprint arXiv:2505.18543 (2025).
Zhihao Dou, Jiaqi Wang, Wei Sun, Zhuqing Liu, and Minghong Fang. Toward Malicious Clients Detection in Federated Learning. In Proc. ACM AsiaCCS, 2025 (*co-primary authors, acceptance rate: 20.4%).
Zhen Qin, Zhuqing Liu, Songtao Lu, Yingbin Liang, and Jia Liu, "DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity," in Proc. ICLR, Singapore, Apr. 2025 (acceptance rate: 32%).
Minghong Fang, Seyedsina Nabavirazavi, Zhuqing Liu, Wei Sun, Sundararaja Iyengar, Haibo Yang, "Do We Really Need to Design New Byzantine-robust Aggregation Rules?," in NDSS, 2025 (acceptance rate: 15%).
Minghong Fang, Zhuqing Liu, Xuecen Zhao and Jia Liu, "Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing," in Proc. ACM TheWebConf (WWW), Sydney, Australia, Apr. 2025.(acceptance rate: 19.8%)
Wenbin Wang, Qiwen Ma, Zifan Zhang, Yuchen Liu, Zhuqing Liu, and Minghong Fang, "Poisoning Attacks and Defenses to Federated Unlearning," in Proc. ACM TheWebConf (WWW), Sydney, Australia, Apr. 2025.(acceptance rate: 19.8%)
Baolei Zhang*, Haoran Xin*, Minghong Fang, Zhuqing Liu, Biao Yi, Tong Li, and Zheli Liu. Traceback of Poisoning Attacks to Retrieval-Augmented Generation. In Proc. The Web Conference (WWW), 2025 (*co-primary authors, acceptance rate: 19.8%).
Zhihao Dou, Xin Hu, Haibo Yang, Zhuqing Liu, and Minghong Fang. "Adversarial Attacks to Multi-Modal Models," In Proc. ACM LAMPS, 2024.
Zhuqing Liu, Xin Zhang, Jia Liu, Zhengyuan Zhu, and Songtao Lu, "PILOT: An O(1/T)-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation," in Proc. ICLR, Vienna, Austria, May. 2024, Spotlight (acceptance rate: 5%).
Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, and Jia Liu, "Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning," in Proc. ICML, Honolulu, HI, Jul. 2023 (acceptance rate: 27.9%).
Zhuqing Liu, Xin Zhang, Songtao Lu, and Jia Liu, "PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities," in Proc. ACM MobiHoc, Washington, DC, Oct. 2023 (acceptance rate: 21.9%).
Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, and Jia Liu, "INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks," in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).
Zhuqing Liu, Xin Zhang, and Jia Liu, "SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters," in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).
Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, and Zhengyuan Zhu, "NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data," in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).
Haibo Yang, Zhuqing Liu, Xin Zhang, and Jia Liu, "SAGDA: Achieving O(ε-2) Communication Complexity in Federated Min-Max Learning," in Proc. NeurIPS, New Orleans, LA, Dec. 2022 (acceptance rate: 25.6%).
Zhuqing Liu*, Xin Zhang*, Jia Liu, Zhengyuan Zhu, and Songtao Lu, "Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning," in Proc. NeurIPS, Virtual Event, Dec. 2021 (acceptance rate: 26%).
Peiwen Qiu, Yining Li, Zhuqing Liu, Prashant Khanduri, Jia Liu, Ness B. Shroff, Elizabeth S. Bentley, and Kurt Turck, "DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization," in Proc. IEEE INFOCOM, New York City, NY, May 2023 (acceptance rate: 19.2%).
Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, and Michinari Momma "Federated Multi-Objective Learning," in Proc. NeurIPS, New Orleans, LA, Dec. 2023 (acceptance rate: 26.1%).
Luning Bi, Fei Tao, Ying Zuo, Zhuqing Liu. “Energy-aware material selection for product with multi-component under cloud environment”, ASME Journal of Computing and Information Science in Engineering, 2017.
Zhuqing Liu, Haibin Duan, Yijun Yang, Xiaoguang Hu. "Pendulum-like Oscillation Controller for UAV Based on Levy-flight Pigeon-inspired Optimization and LQR", IEEE Symposium Series on Computational Intelligence, 2016. (acceptance rate: 33%).

*The authors are joint first authors with equal contributions.

Academical Services

Served as Reviewer for KDD 2025, TMC 2025, NeurIPS 2024, IEEE Transactions on Signal Processing 2024, JSAIT 2023, TNSE 2023, INFOCOM 2023, ICML 2022, NeurIPS 2022, AAAI 2022.