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Object DetectionI am an undergraduate student majoring in Big Data and Artificial Intelligence, with experience in data processing, machine learning, and AI-related research. I have worked with Python, SQL, Pandas, Scikit-learn, PyTorch, and Jupyter Notebook for data cleaning, preprocessing, model training, and evaluation. In my previous projects, I completed medical image data labeling and model training using YOLO, and I also participated in Kaggle and AI competitions involving toxic comment classification, feature engineering, model tuning, and ensemble methods. I also have research experience in graph learning and AI for Science through a paper currently under review at IEEE TKDE, where I contributed to methodology design, validation, data curation, formal analysis, and writing. These experiences have helped me develop strong attention to detail, logical judgment, and the ability to evaluate data quality and AI-generated outputs. As a native Mandarin Chinese speaker with English reading and writing ability, I am confident in performing Chinese data annotation, LLM response evaluation, AI training data review, and bilingual annotation tasks.
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ClassificationI worked on a Chinese text annotation practice project involving short user comments and social media-style text. The main tasks included classifying text into predefined categories, identifying sentiment polarity, and checking whether the assigned labels were consistent with the annotation guidelines. I focused on accuracy, consistency, and clear judgment when handling ambiguous Chinese expressions.
TextI contributed to evaluation and annotation tasks related to LLM response quality and assessment of model reasoning. My work included Chinese and English data labeling and review for model output evaluation and technical content quality checks. This role enhanced my attention to linguistic detail and model fairness. • Evaluated LLM-generated text responses for accuracy and appropriateness. • Performed bilingual data annotation tasks in Chinese and English. • Reviewed technical content for clarity and correctness. • Supported prompt quality assessment and model reasoning analysis.
Object DetectionI performed medical image data labeling for a YOLO-based object detection model as part of a self-driven project. My work included carefully annotating images, ensuring high-quality labeled data for model training, and evaluating the model's recognition accuracy. All annotations contributed directly to supervised learning for medical imaging classification. • Labeled medical images using bounding boxes for object detection tasks. • Ensured data quality and consistency throughout the annotation process. • Supported model training that achieved over 85% accuracy on validation data. • Gained hands-on experience with annotation workflows relevant to the medical domain.
Bachelor of Science, Computer Science and Artificial Intelligence
AI Model Training Project Assistant
Undergraduate Researcher
