B.Eng. in Artificial Intelligence
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Research Assistant, HKUST
I received my B.Eng. in Artificial Intelligence from Sun Yat-sen University and am currently a Research Assistant at HKUST, focusing on AI system deployment, speech and multimodal interaction, intelligent applications in healthcare, and full-stack development bridging research and engineering.
B.Eng. in Artificial Intelligence
Research Assistant
Python, TypeScript, Kotlin, Java, C++, PyTorch, TensorFlow, JAX, Vue, Spring Boot, Docker, Linux
Experienced in the full engineering pipeline from model training and backend services to frontend interaction and deployment.
Longitudinal behavioral data tracking and psychological assessment platform for pregnant women, featuring automated task scheduling and a real-time monitoring dashboard.
AI-powered smoking cessation support system with personalized content generation and long-term behavioral tracking.
LLM-based colonoscopy screening guidance system with RAG-powered medical Q&A, risk assessment, and customized PDF report generation.
Hybrid vision system combining traditional CV algorithms and lightweight deep learning for precise fabric area measurement on low-power embedded devices.
Android application for electronic scale label design with rich layout editing, barcode/QR code support, and distributed local-remote data storage.
Full-stack web application with a face editing canvas, real-time multi-angle preview, and decoupled SpringBoot + Flask architecture. Second Prize, South China Region.
Dynamic 3D human reconstruction from monocular RGB video, leveraging depth priors and parametric body models (SMPL/SMPL-X) to improve anatomical accuracy.
RAG-based medical Q&A system integrating professional documents and community data via LangChain, Chroma, and BERTopic for context-aware follow-up generation.
Optimized NeRF framework with a two-stage training strategy (grayscale then RGB), reducing training time by 5–10% while maintaining reconstruction quality.