I am a Postdoctoral Fellow in the CS Department at City University of Hong Kong, advised by Prof. Qingfu Zhang (Chair Professor, IEEE Fellow).

My research focuses on computational intelligence approaches to build advanced AI systems. Specifically, I develop evolutionary computation methods and machine learning algorithms for solving complex optimization problems in real-world applications.

Throughout my academic journey, I have had the privilege of collaborating with lots of distinguished researchers: Prof. Qingchuan Zhao, Dr. Xi Lin, and Mr. Cheng Gong.

🔥 News

• 2025.03: 🎉🎉 Our paper on multi-objective adversarial attack is accepted by CVPR2025.
• 2024.11: 🎉🎉 The paper on exploring the adversarial frontier is accepted by IEEE TETCI.

📝 Publications

• CVPR 2025 MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
  P. Guo, C. Gong, X. Lin, F. Liu, Z. Lu, Q. Zhang, Z. Wang
  Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [Arxiv] [CVPR2025]

• IEEE TETCI Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
  P. Guo, C. Gong, X. Lin, Z. Yang, Q. Zhang
  IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI). [Arxiv] [IEEE TETCI]

• GECCO 2024 L-AutoDA: Large Language Models for Automatically Evolving Decision-based Adversarial Attacks
  P. Guo, F. Liu, X. Lin, Q. Zhao, Q. Zhang.
  The Genetic and Evolutionary Computation Conference (GECCO), 2024. [Arxiv] [GECCO’24]

• EMO 2023 Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables
  P. Guo, Q. Zhang, X. Lin.
  International Conference on Evolutionary Multi-Criterion Optimization (EMO), 2023. [Arxiv] [EMO’23]

• USENIX Sec. 2021 Stars Can Tell: A Robust Method to Defend against GPS Spoofing Attacks using Off-the-shelf Chipset
  S. Liu, X. Cheng, H. Yang, Y. Shu, X. Weng, P. Guo, K. (Curtis) Zeng, G. Wang, Y. Yang
  The 30th USENIX Security Symposium (USENIX Security), 2021. [USENIX Security]

• PPSN 2024 LTR-HSS: A Learning-to-Rank Based Framework for Hypervolume Subset Selection
  C. Gong, P. Guo, T. Shu, Q. Zhang & H. Ishibuchi
  International Conference on Parallel Problem Solving from Nature (PPSN), 2024. [PPSN’24]

• IEEE TEVC DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization
• C. Gong, Y. Nan, K. Shang, P. Guo, H. Ishibuchi, Q. Zhang
  IEEE Transactions on Evolutionary Computation (TEVC). [TEVC]

📄 Preprints

• CoEvo: Continual Evolution of Symbolic Solutions Using Large Language Models
  P. Guo, Q. Zhang, X. Lin
  ArXiv, 2024. [Arxiv]

• PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks
  P. Guo, Z. Yang, X. Lin, Q. Zhao, Q. Zhang.
  ArXiv, 2024. [Arxiv]

• A Systematic Survey on Large Language Models for Algorithm Design
  F. Liu, Y. Yao, P. Guo, Z. Yang, Z. Zhao, X. Lin, X. Tong, M. Yuan, Z. Lu, Z. Wang, Q. Zhang
  ArXiv, 2024. [Arxiv]

👓 Activities

Conference Reviewer

• [CEC] The IEEE Congress on Evolutionary Computation, 2024
• [NIPS] In Advances in Neural Information Processing Systems, 2024, 2025
• [ICML] International Conference on Machine Learning, 2025
• [ICLR] International Conference on Learning Representations, 2025
• [AISTATS] International Conference on Artificial Intelligence and Statistics, 2025

Journal Reviewer

• [SWEVO] Swarm and Evolutionary Computation
• [ECJ] Evolutionary Computation