1. The President's Award, Full-volume quality inspection image storage with thousand-fold compression technology, 2024.
2. The Innovation Star Award, Unsupervised anomaly detection for battery cell appearance, 2024.
3. Papers:
- STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment, TIP, 2025
- FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis, NeurIPS, 2025
- ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection, TAI, 2025
- Enhancing Multimodal Learning via Hierarchical Fusion Architecture Search with Inconsistency Mitigation, IEEE TIP, 2025
- SARD: Segmentation-Aware Anomalies Synthesis via Region Constrained Diffusion with Discriminative Mask Guidance, ICMIANDC, 2025
- Trade-offs in Image Generation: How Do Different Dimensions Interact?, ICCV, 2025
- Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning
Research Experience
Currently an Algorithm Manager in the Department of Intelligent Manufacturing at CATL. Previously, his research focused on anomaly detection and localization for both industrial and medical images.
Education
PhD in Machine Learning, University of Surrey, NICE Group, supervised by Prof. Yaochu Jin.
Background
Research Interests: AI for Manufacturing and Batteries. Professional Field: AI applications in smart manufacturing and batteries.