AI-assisted research
Designing workflows where AI supports data retrieval, transformation, statistical analysis, visualization, and reproducible reporting while keeping decisions inspectable.
I’m Akewak Jeba, a data scientist and scientific software developer at the intersection of artificial intelligence, cultural heritage, computational biology, and reproducible research.
$ whoami → data scientist_
R · Python
AI + Research
Open Source
My work combines statistical computing, machine learning, APIs, software engineering, and open research infrastructure. I focus on making complex datasets easier to retrieve, analyze, validate, and reuse.
Designing workflows where AI supports data retrieval, transformation, statistical analysis, visualization, and reproducible reporting while keeping decisions inspectable.
Building open-source R tooling and data pipelines for cultural heritage metadata, bibliographic data, omics, and interactive analysis.
Connecting APIs, structured metadata, statistical methods, CI/CD, documentation, and scalable computing into research workflows that others can rerun.
An R interface for programmatic access to the Finna API, enabling reproducible retrieval and analysis of Finnish library, archive, and museum metadata.
Tools for working with Finnish ontology and vocabulary services, supporting concept search, semantic enrichment, and multilingual cultural heritage workflows.
Computational access and analysis workflows for Finnish national bibliography data, supporting large-scale studies of publishing and literary history.
Contributions to statistical and interactive tools for robust differential expression analysis and exploration of hierarchical biological data.
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Live repository information will appear here.
I’m always interested in useful collaborations where rigorous data science and good software engineering meet.