Junhao Cai (蔡俊豪)
M.S. StudentShenzhen University ResearchMultimodal Learning & Anomaly Detection Open toPh.D. Opportunities
Building multimodal and diffusion models for robust anomaly detection.
I am a Master's student in the College of Mechatronics and Control Engineering at Shenzhen University. My research focuses on multimodal learning, diffusion models, industrial anomaly detection, hypergraph neural networks (HGNN), and LoRA fine-tuning. I develop parameter-efficient adaptation strategies and cross-modal architectures for robust visual understanding, with text-to-3D generation as a complementary direction.
My academic excellence has been recognized with several distinctions, including being named an 🏆 Outstanding Graduate of Shenzhen University (2025, awarded to 333 out of 5,208 graduates) and receiving the 🏆 Bachelor of Honor from the School of Mechatronics and Control Engineering (2025), through sustained academic leadership, service, and interdisciplinary innovation.
Detailed Profile (详细个人介绍)
I am a Master's student in the College of Mechatronics and Control Engineering at Shenzhen University. My work explores multimodal learning, diffusion models, industrial anomaly detection, hypergraph neural networks, and parameter-efficient LoRA fine-tuning. Text-to-3D generation remains a complementary direction.
My academic distinctions include Outstanding Graduate of Shenzhen University (2025, awarded to 333 of 5,208 graduates) and Bachelor of Honor from the School of Mechatronics and Control Engineering. I am currently open to Ph.D. opportunities in computer vision, multimodal learning, generative AI, and embodied intelligence.
研究方向包括多模态学习、扩散模型、工业异常检测、超图神经网络（HGNN）与 LoRA 微调，重点关注参数高效适配策略和跨模态网络架构。
