I am a doctoral candidate in the CS Department at 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, and Mr. Cheng Gong.

🔥 News

• 2024.11: 🎉🎉 The paper on exploring the adversarial frontier is accepted by IEEE TETCI.
• 2024.11: 🎉🎉 The paper on the automated design of using LLMs is available at Arxiv.
• 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

• 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 (TETCI). [Arxiv]

• 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 Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
• C. Gong, Y. Nan, K. Shang, P. Guo, H. Ishibuchi, Q. Zhang
  IEEE Transactions on Evolutionary Computation (TEVC). [TEVC]

📄 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]

• 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
• [ICLR] International Conference on Learning Representations, 2025
• [AISTATS] International Conference on Artificial Intelligence and Statistics, 2025

Journal Reviewer

• [SWEVO] Swarm and Evolutionary Computation