I am a doctoral candidate in the Department of Computer Science at City University of Hong Kong (CityU), advised by Prof. Qingfu Zhang (Chair Professor, IEEE Fellow).

My research focuses on harnessing computational intelligence to develop advanced AI systems. Presently, my work is concentrated on tackling optimization challenges and devising algorithms that contribute to the robust and trustworthy AI systems.

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

I am always on the lookout for passionate individuals to collaborate with. If you share an interest in advancing AI systems and would like to work together, please reach out to me at pingguo5-c at my dot cityu dot edu dot hk.

🔥 News

• 2024.05: 🎉🎉 The paper on the automated design of adversarial attacks algorithms using LLMs has been accepted by GECCO’24.
• 2023.03: 🎉🎉 The paper on properties of sharing in multi-objective optimization has been accepted by EMO’23.

📝 Publications

• 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]

• 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]

📄 Preprints

• 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]

• Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
  P. Guo, C. Gong, X. Lin, Z. Yang, 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
• [ICLR] International Conference on Learning Representations, 2025

Journal Reviewer

• [SWEVO] Swarm and Evolutionary Computation