Summary
Jacob Kaplan is a data-driven research specialist and founder with a Ph.D. in Criminology who applies advanced engineering techniques to public safety and voting research. Based at Princeton University and the Princeton School of Public and International Affairs, he leverages large-scale geospatial mobility data and probabilistic record linkage to answer policy-relevant questions about policing, homelessness, and civic participation. He builds automated NLP pipelines with LLMs to classify and cluster messy police data and has cleaned and harmonized multi-agency datasets from nearly 100 departments. As founder of 1930 Research LLC, he turns complex CAD/RMS and survey data into analysis-ready products for decision-makers. His work has informed federal policy deliberations—he served as a voting member on the FBI’s UCR subcommittee—and he pairs rigorous academic methods with production-grade ETL and code-review practices. Based in the New York City area, he blends academic rigor, practical data engineering, and a privacy-first mindset to make administrative data usable and trustworthy.
10 years of coding experience
California State University, Sacramento
PhD, Criminology, PhD, Criminology at University of Pennsylvania