Public Health and Health Professions
Prosperi Lab
Led by Mattia Prosperi · Epidemiology
What we study
The Prosperi Lab uses artificial intelligence and big data to study viral and bacterial diseases, focusing on predictive models and causal AI methods. Their work integrates health data, epidemiology, and computational tools to improve public health outcomes.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Projects
- 5
- Papers
- 5
Current projects
Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning framework
Combines viral genetics and deep learning to forecast how HIV transmission networks will evolve, to guide prevention efforts.
SYNcronized Agentic Prescriptive System for Improving Sequentially HIV Care Continuum (SYNAPSIS-HIV-CC)
Develops an AI system that coordinates steps across the HIV care continuum, from testing through treatment, to help keep patients connected to care.
Show 3 more projectsShow fewer
An AI-based behavioral intervention to improve pre-exposure prophylaxis for HIV prevention
Builds an AI-based behavioral intervention aimed at helping people use pre-exposure prophylaxis (PrEP), a medication that prevents HIV, consistently.
Harnessing big data to understand the confluence of HIV and cancer stigma in the US Southeast
Analyzes data to explore HIV and cancer stigma in the Southeast.
Interoperable phenotyping of high impact chronic pain tailored to rehabilitation
Uses AI to improve chronic pain management through data integration.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Evaluation of causal reasoning for large language models in contextualized clinical scenarios of laboratory test interpretation
2026 · npj Digital Medicine
Tests whether large language models can interpret lab test results correctly.
- Consistency in causal reasoning for large language models in scenarios of HIV antiretroviral treatment, drug interactions, and side effects
2026 · npj Digital Medicine
Assesses how well large language models reason about HIV treatment and drug interactions.
Show 3 more papersShow fewer
- Real-world evaluation of a transformer-based natural language processing system for identifying social determinants of health from routine clinical documentation
2026 · JAMIA Open
Evaluates a system for extracting social determinants of health from clinical notes.
- Assessing the effects of sociodemographic, clinical, and mental health conditions on COVID severity and duration
2026 · BMC Infectious Diseases
Identifies factors linked to COVID severity and duration.
- Analyzing determinants of biological age through systematic review of prediction models
2026 · BMC Medical Informatics and Decision Making
Reviews models that estimate biological age from routine blood tests.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Mattia Prosperi hasn't set up this page yet. Their UF Scholars profile lists their publications.