Research Scientist
Caleb Bowyer is a PhD candidate in engineering at the University of Florida, and his dissertation research is about determining how to best train cooperative autonomous agents from noisy and partially observed data for decision making tasks. At the LPRC, he is head of the LPRC’s DETECT research initiative and co-leader of the Data Analytics Working Group (DAWG). He researches, develops, and implements AI solutions related to detecting theft, fraud, or violence attempts earlier and further away from retail stores. For examples, his research spans everything from web scraping social media sites or forums online in Zone 5, to utilizing license plate reader technology in Zone 4, to running various object detection models in Zone 3, to trialing audio threat detection in Zone 2, down to what are the best protocols for Body-Worn Cameras in Zone 1 inside of a retail store. His dream goal at the LPRC is to integrate all of the AI sensors and models across the five zones of influence, once they are all sufficiently developed, into a production ready system for better alerting of offenders in real time and detecting repeat offenders on their journey to commit harm and then deflecting that harm. Caleb is also highly interested in new OSINT methodologies and leveraging better OSINT tools to help with improve outcomes in investigations and for retroactive studies of incidents. His list of research projects at the LPRC is ever evolving and expanding.