Luay E.

Key Skills

Freelancer Overview

While I do not have formal professional experience in AI data labeling, my academic and research background has involved many of the same skills required for evaluating and improving AI systems. As a graduate researcher at UC Berkeley, I developed Python-based workflows to process large remote sensing datasets and applied machine learning techniques, including Random Forest models, to distinguish true hydrological signals from satellite artifacts. This work required careful validation of outputs, systematic testing of computational methods, and rigorous documentation to ensure the reliability and reproducibility of results. In addition, as a Graduate Student Instructor at UC Berkeley, I taught Python and numerical methods to engineering students, guiding them through debugging, algorithm development, and the evaluation of computational solutions. These experiences strengthened my ability to critically assess technical outputs, identify errors and edge cases, and communicate complex concepts clearly. I am excited about applying these analytical and problem-design skills to projects focused on testing and improving frontier AI models.

Labeling Experience

Education

Work History