Tommy Carstensen is a bioinformatician and data scientist whose website brings together interactive data projects, public records research, archival preservation, machine learning, and data visualization. His work demonstrates how computational tools can transform large and complicated datasets into searchable, visual, and more accessible research resources.
Of particular interest to investigators is Carstensen’s work involving public records and the Epstein files. His site includes an EFTA: Public Records & Evidence project focused on records released under the Epstein Files Transparency Act. The project uses technologies including Python, OpenCV, and graph based analysis to organize and examine archival material.
Carstensen has also developed significant digital preservation projects related to the January 6 attack on the U.S. Capitol. His website highlights both a crowdsourced January 6 research project incorporating human verification and a large video archive created through video collection and metadata indexing. These projects provide useful examples of how open source researchers can preserve digital evidence while creating structured datasets for later investigation.
Beyond archival investigations, the site features interactive projects involving elections, financial history, public health, clinical research, geographic data, and financial markets. Carstensen describes his professional specialization as electronic health record data engineering, high performance visual analytics, and reproducible machine learning pipelines. His technical toolkit includes Python, Dask, Parquet, Bokeh, Datashader, Plotly, PyTorch, Azure Machine Learning, and Google Cloud.
For the Resistance Directory, Tommy Carstensen’s website is particularly valuable as an example of advanced data driven investigation. Researchers interested in the Epstein files, government records, digital archives, large datasets, open source intelligence, and evidence visualization can explore both his investigative projects and the technical approaches used to build them.
Key Features
- Provides data driven public records and archival research projects.
- Includes an Epstein Files Transparency Act public records and evidence project.
- Uses computational methods to organize and analyze large collections of information.
- Includes January 6 archival and crowdsourced documentation projects.
- Demonstrates digital evidence preservation and metadata indexing techniques.
- Features interactive data visualizations covering elections, finance, health, and historical events.
- Uses Python, OpenCV, graph theory, machine learning, and visualization technologies.
- Provides examples of reproducible approaches to complex investigative datasets.
Best For
Investigative researchers, data journalists, Epstein files researchers, open source investigators, archivists, data scientists, programmers, academic researchers, digital preservation projects, and people interested in using computational methods for public interest investigations.