Data Label
Data annotation and video annotation
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I am a detail-oriented software developer with hands-on experience in data labeling, annotation, and AI training data workflows, particularly within academic research and machine learning contexts. My background includes supporting computer vision research at UCLA, where I executed complex labeling tasks such as bounding boxes, polygons, key point mapping, and semantic segmentation to generate high-fidelity datasets for supervised learning and temporal action detection projects. I am proficient with industry-standard annotation tools like CVAT, Labelbox, and Atlas Capture, and have a strong foundation in Python, MATLAB, and Excel for data processing and visualization. My work emphasizes precision in data boundaries to mitigate model hallucination risks and ensure training data quality, and I am adept at collaborating in multidisciplinary teams to deliver reliable, well-structured datasets for AI and ML applications.
Data annotation and video annotation
Bachelor of Science, Computer Science
High School Diploma, General Education
Software Developer
Full-Stack Developer Intern