Brief introduction: AI Research Engineer – multimodal & knowledge-enhanced systems. Responsibilities: Built production-oriented Graph RAG pipelines (vector DB, chunking, embedding optimization, retrieval tuning), which improved retrieval accuracy and consistence, and enhanced structured reasoning for clinical knowledge bases. (Paper under review) Conducted research on generative AI models (diffusion models, GANs) for synthesizing medical and time-series data, with my proposed FHRDiff model achieving an overall 75.3% performance improvement over prior state-of-the-art methods. Paper published Developed an automated OCR pipeline for extracting blood pressure monitor readings, reducing manual transcription workload and significantly improving clinical data digitization efficiency. Utilized Weights & Biases for experiment tracking and model evaluation, improving reproducibility and reducing debugging/training iteration time. Curated and preprocessed multimodal datasets (text, image, signal, and knowledge-graph data), enabling cleaner data pipelines and reducing preprocessing effort for downstream ML/LLM workflows.

Brief introduction: Deep learning research engineer – medical diagnostics. Responsibilities: Contributed to multiple AI and computer vision projects for medical diagnostics, improving classification and segmentation performance by 5% through model refinement and data-quality improvements. Trained, fine-tuned, and evaluated deep learning models including U-Net, YOLOv5, ResNet, and diffusion transformers, achieving measurable gains in accuracy and robustness through systematic augmentation, hyperparameter optimization, and cross-validation. Collaborated using Git and Git Flow for version control and experiment tracking, improving code reproducibility and team development efficiency in agile project settings.

Brief introduction: Embedded system development and functional testing for dedical devices. Responsibilities: Designed and implemented embedded software for a simulator using C language and STM32 microcontrollers, enabling automated functional testing and improving test efficiency. Evaluated functional and business requirements for multiple medical device lines (patient monitors, ECG, OB/GYN, etc.), designed comprehensive test cases, and performed functional verification using both manual and automated testing frameworks.

Brief introduction: I participated in the project ”Effect of Nutritional Status of Pregnant Women on Fetal Health”, and I collected data and preprocessed the samples to ensure the data reliability and integrity of the follow-up clinical trails, which is essential to the project. Responsibilities: Participated in the project “Effect of Nutritional Status of Pregnant Women on Fetal Health”. Communicated with doctors and target pregnant women in hospital, requested their consent to provide data for the project to ensure the data reliability and integrity of the following-up clinical trails. Completed the collection of data and pre-preprocessed samples via centrifugation and separation. Designed a questionnaire survey and completed data analysis with SPSS and Matlab software. Wrote academic paper (Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing), and published in a high-quality journal as the first author.