Contributed to AI model evaluation and refinement under Project Phoenix and Project Hydra on outlier platform
Defining new rubric criteria for stressing testing AI models.
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With a strong background in advanced materials research and data-driven experimental analysis, I have extensive experience in data processing, annotation, and curation, particularly in the context of scientific and environmental datasets. My work has involved managing and organizing large volumes of experimental data using tools such as MS Excel and Origin, ensuring accuracy and consistency for research publications and collaborative projects. I have developed and optimized data pipelines for material synthesis experiments, environmental monitoring, and sensor development, which required meticulous labeling and validation of datasets for further analysis and modeling. My attention to detail, proficiency in analytical software, and experience planning project strategies make me well-equipped to contribute to high-quality data labeling and annotation tasks, supporting the development of reliable AI training data across scientific and technical domains.
Defining new rubric criteria for stressing testing AI models.
Doctor of Philosophy, Material Chemistry
Master of Science, Organic Chemistry
Research Engineer
Senior Researcher