Zixuan X.

Key Skills

Software

Don't disclose

Top Subject Matter

Machine learning and data analytics

Web-based validation system (project Transloc)

Top Data Types

Video

Top Task Types

Action Recognition

Freelancer Overview

Corporate Innovation Practice — Machine learning for visual recognition of live surgery broadcasts (segment identificati. Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose, Internal, and Proprietary Tooling. Education includes Student-Engineer, Université de Technologie de Compiègne (UTC) (2024) and Bachelor in Computer Engineering, Shanghai University (SHU) (2024). AI-training focus includes data types such as Video and labeling workflows including Action Recognition, Evaluation, and Rating.

Labeling Experience

Assistant Engineer Intern — Development of a validation system (Transloc)

Video

Built and maintained components of a validation system as part of the Transloc project. Supported the development lifecycle for ensuring the correctness of outputs produced by underlying data/AI processes, including interface modifications and database maintenance. Contributed to operational checks that validated system behavior and related data artifacts used by the application. • Developed validation system logic • Modified and optimized web interfaces • Maintained PostgreSQL database • Supported correctness/validation workflows for project deliverables

2025 - 2025

Corporate Innovation Practice — Machine learning for visual recognition of live surgery broadcasts (segment identification & automatic editing)

Don't discloseVideoAction Recognition

Developed a machine-learning based visual recognition approach to analyze live surgery broadcasts and identify effective video segments. Performed downstream processing to automatically edit or select relevant parts of live video streams for improved content quality. Work was focused on turning visual cues from video into actionable segment-level outputs for the pipeline. • Analyzed live surgery broadcast content • Used machine learning for visual recognition • Identified effective segments in live videos • Assisted with automatic video editing/selection logic

2024 - 2024

Education

Work History