Our Team
Tudor Vlad
Project Lead, Programmer, UI Dev, AI Integration
Jake Kovacocy
Lead Developer, Mapmaker, AI Integration, Cohesive Concept Lead
Gabriel Antonio Alvarez de Azevedo
Lead Researcher, Programmer
Imoni Ahmad
Data Engineer, Public Outreach Lead, Developer
About AI Humanology
AI Humanology began as a student-built health equity project created by three
junior-year students from The Bolles School: Gabriel Azevedo, Jake Kovacocy, and
Tudor Andrei Vlad.
The idea was born during the 2026 CodeforAwhile Data for Good AI Challenge, a
regional AI competition organized by the University of North Florida's Florida Data
Science and Social Good program in partnership with the City of Jacksonville. The
challenge asked students to take on more than a coding assignment — it invited them
to use artificial intelligence, large datasets, and critical thinking to confront
real problems affecting local communities.
At the center of the competition was AI-augmented problem solving: learning how to
use artificial intelligence as a "force multiplier" to uncover actionable insights,
strengthen analysis, and turn complex data into practical solutions.
We chose to focus on health equity. Our original project analyzed
Jacksonville/Duval County area data to help users understand how ZIP codes affect
access to resources and how personal health factors can influence life expectancy.
Our work earned us first place in the competition. What began as a local health
equity tool quickly became the foundation for something larger.
AI Humanology has since grown into a broader social-impact initiative for the state
of Florida. The platform now allows users across Florida to assess their health
equity and life expectancy risk while connecting them with proximity-based health
recommendations and resources.
The project has also expanded beyond its original team, with our new teammate Imoni,
a junior student at The John Cooper School in The Woodlands, Texas, who joined the
effort to help bring AI Humanology to a statewide resource.
By combining public data, personal health inputs, and AI-supported analysis, AI
Humanology became more than a student project. It is a social enterprise built
around a simple but powerful idea: technology should help people better understand
their health, their communities, and the resources available to them.
For us, AI Humanology represents the kind of innovation we can believe in —
human-centered, data-driven, and designed to create meaningful impact.
Data Sources
Boundaries: U.S. Census Bureau TIGER/Line, all 983 Florida ZCTAs and 67 counties.
Population, poverty, uninsured rate, SNAP/food assistance: Census ACS 2018–2022
5-year estimates, pulled live from api.census.gov, ZIP-code level.
Life expectancy: NCHS/USALEEP (2010–2015 estimates), averaged from real census tracts
within each ZIP's boundary using 4,245 actual Florida tract shapes — real ZIP-level values
for 833 ZIPs; the 116 ZIPs with no reportable tract estimate use their county average
(County Health Rankings & Roadmaps 2024). CDC does not publish life expectancy at the ZIP
level directly, so this is the most granular real reconstruction available.
ZIP → county / ZIP → tract assignment: computed geometrically from each ZIP's or
tract's official Census centroid against real boundary polygons.
