Abdul User

Reviewed and categorized 10,000+ images and performed frame-by-frame video analysis to identify and log motion events. Maintained spreadsheet annotation logs, applied consistent taxonomy rules across differing schemas, and flagged ambiguous cases for secondary review.

Reviewed and categorized 10,000+ images across personal and community dataset exercises using consistent taxonomy rules. Performed frame-by-frame video analysis to log motion events, maintained spreadsheet-based annotation logs, and followed written guidelines across changing schemas with zero retraining required after briefing.

Evaluated thousands of user-submitted images and short-form videos daily against community policy criteria, using written rationale to flag edge cases. Sustained 60–90 minute uninterrupted review sessions while meeting daily accuracy thresholds.

Evaluated thousands of user-submitted images and short-form videos daily against community policy criteria, flagging edge cases with written rationale. Maintained sustained attention during 60–90 minute sessions while meeting daily accuracy thresholds and contributing to internal guideline refinement.

Coded qualitative survey data using a predefined rubric, converting unstructured responses into machine-readable categories. Cross-verified entries with a second reviewer and resolved inter-coder disagreements through documented logic to improve coding consistency.

Earned a Bachelor of Science in Computer Science, with coursework including Data Structures, Digital Image Processing, Human-Computer Interaction, and Probability & Statistics.