CV

Education

• B.S. in Computer Science (Elite Class), Nankai University, Sep. 2023 – Present
  • GPA: 3.5 / 4.0
  • Advisors: Prof. Chongyi Li & Prof. Chunle Guo
  • Teaching Assistant: Introduction to Artificial Intelligence (Fall 2025)
  • Honor: Nankai University Innovation Scholarship

Research Experience

• AIGC Text Detection (Jan. 2025 – Present)
  • VCIP Lab, Nankai University
  • Built a more robust machine-generated text detection and evaluation system. Proposed a novel optimization strategy that reduces computation cost while improving performance by ~70% relative to previous SOTA. Also proposed a comprehensive benchmark dataset for robust evaluation.
  • Result: Paper DetectAnyLLM accepted at ACM MM 2025 (CCF-A) as Oral.
• Text/Image to SVG (Sep. 2025 – Present)
  • VCIP Lab, Nankai University
  • Investigating limitations of existing text/image-to-SVG models (e.g., OmniSVG, InternSVG). Exploring Continue-Pretraining and SFT+GRPO strategies to improve LLM understanding of SVG data.
• Text-to-Image Quality Assessment (Sep. 2024 – Nov. 2024)
  • VCIP Lab, Nankai University
  • Constructed a large-scale fine-grained T2I dataset and fine-tuned BLIP for human-aligned scoring with fine-grained capability. Benchmarked 20+ T2I models.
  • Contribution: Responsible for structural image quality evaluation task and contributed to the innovative BLIP fine-tuning strategy design.
  • Result: Paper EvalMuse-40K accepted at AAAI 2026 (CCF-A).

Publications

DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models

Fu, J., Guo, C., & Li, C. (2025). DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models. ACM MM 2025. Oral.

ViDA-UGC: Detailed Image Quality Analysis via Visual Distortion Assessment for UGC Images

Liao, W., Yuan, J., Xu, Y., Guo, C., Zhang, Z., Li, J., Fu, J., et al. (2025). ViDA-UGC: Detailed Image Quality Analysis via Visual Distortion Assessment for UGC Images. arXiv preprint arXiv:2508.12605.

EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation

Han, S., Fan, H., Fu, J., Li, L., Li, T., et al. (2024). EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation. AAAI 2026.

Awards & Competitions

• National Special Prize, Challenge Cup “AI+” Track (Oct.–Nov. 2025)
  • Project: “婴语新声” — Multimodal LLM-based Infant Behavior Semantic Analysis and Monitoring System
• National First Prize, CCF-TCARCH Architecture Challenge (Sep. 2025)
• Nankai University Innovation Scholarship

Service

• Reviewer: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)