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Top Subject Matter

Evolutionary biology

Genomics

Bioinformatics

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Top Task Types

Computer Programming/Coding

Data Collection

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Freelancer Overview

While my background is in computational biology rather than commercial AI training data, I have extensive experience in the curation, quality control, and structured annotation of large-scale biological datasets — work that is functionally analogous to data labeling pipelines. Over 10+ years, I have developed and validated reproducible workflows for processing high-dimensional NGS data (genomics, transcriptomics, proteomics), ensuring data integrity and consistency across heterogeneous multi-omics datasets. I am experienced in defining annotation schemas, applying them systematically at scale, and resolving edge cases through domain expertise — skills that transfer directly to structured data labeling for AI applications. A concrete example of ground-truth creation for machine learning purposes is my co-first-author work published in Nature (2023), where I generated computational predictions from multi-omics data that were subsequently validated experimentally in two model organisms — in effect, producing a labeled, validated dataset linking molecular features to biological outcomes. I also have a strong track record of collaborative work bridging domain experts and technical teams, mentoring researchers in computational methods, and communicating complex results clearly. I am confident in applying this rigorous, systematic approach to AI training data contexts.

Labeling Experience

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