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pgg3/README.md

Ping Guo

Google Scholar ResearchGate Personal  Website CityU

πŸ‘¨β€πŸŽ“ About Me

Postdoctoral Fellow at the Department of Computer Science, City University of Hong Kong, working with Prof. Qingfu Zhang (IEEE Fellow). My research bridges evolutionary computation and machine learning to tackle complex optimization challenges.

πŸ”¬ Research Interests

  • Evolutionary Computation: Multi/Many-objective optimization, algorithm design
  • Adversarial Machine Learning: Robustness evaluation, attack generation, defense strategies
  • LLM for Optimization: Automated algorithm design, symbolic solution evolution

πŸ“š Selected Publications

2025

  • [CVPR] MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework [Code]
  • [IEEE TETCI] Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume

2024

  • [GECCO] L-AutoDA: Large Language Models for Automatically Evolving Decision-based Adversarial Attacks [Code]
  • [Preprint] CoEvo: Continual Evolution of Symbolic Solutions Using Large Language Models [Code]

2023

  • [EMO] Approximation of a Pareto Set Segment Using a Linear Model

πŸ› οΈ Featured Projects

πŸš€ EvoToolkit

LLM-driven evolutionary optimization framework implementing the CoEvo methodology for continual solution evolution.

  • 🎯 Applications: Symbolic regression, algorithm discovery, automated optimization

πŸ€– L-AutoDA

Automated adversarial attack generation using Large Language Models (GECCO 2024)

  • πŸ† Conference: GECCO 2024
  • πŸ“Š Benchmark: State-of-the-art on multiple adversarial robustness benchmarks

πŸ“– Academic Service

Conference Reviewer: NIPS β€’ ICML β€’ ICLR β€’ AISTATS β€’ CEC

Journal Reviewer: Swarm and Evolutionary Computation β€’ Evolutionary Computation

πŸ“Š GitHub Stats

GitHub Stats Top Languages

πŸ“« Contact

  • Email: [email protected]
  • Office: Department of Computer Science, City University of Hong Kong

Pinned Loading

  1. evotoolkit evotoolkit Public

    LLM-driven solution evolutionary optimization toolkit

    Python 177 23

  2. L-AutoDA L-AutoDA Public

    Python 3