Public Health and Health Professions
Marini Lab
Led by Simone Marini · Epidemiology
What we study
The lab uses AI and computational tools to study HIV, SARS-CoV-2, antimicrobial resistance, and inflammation. They develop models to predict disease spread, diagnose infections, and analyze genetic data.
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 with deep learning to forecast how HIV transmission networks will grow and change, to help target 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 to treatment, to help patients stay connected to care.
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Causal AI for Predictive Modeling of Dengue Trajectories: A Comprehensive Approach Considering Climate Variability and…
Using AI to predict dengue outbreaks based on climate and public health efforts.
Genomic characterization of antimicrobial resistance and viral co-infection patterns of Streptococcus pneumoniae by…
Analyzing genetic data to study antibiotic resistance and viral infections in bacteria.
Medical Marijuana and chronic musculoskeletal pain in older adults: a multi-level, precision medicine analysis
Studying how medical marijuana affects pain in older adults using precision medicine methods.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- P-1244. PK/PD Target Attainment of Beta-lactams in Patients With Versus Without Obesity
2026 · Open Forum Infectious Diseases
Looks at beta-lactam drug levels in obese patients to see if they reach therapeutic targets.
- Transcriptional programs diverge in aging mouse and human skeletal muscle
2026 · Aging
Compares aging muscle changes in mice and humans to see how they differ.
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- Consistency in causal reasoning for large language models in scenarios of HIV antiretroviral treatment, drug interactions, and side effects
2026 · npj Digital Medicine
Tests if large language models consistently explain HIV drug interactions and side effects.
- Assessing the effects of sociodemographic, clinical, and mental health conditions on COVID severity and duration
2026 · BMC Infectious Diseases
Studies factors linked to severe COVID-19 symptoms and long-term effects.
- SARITA: a large language model for generating the S1 subunit of the SARS-CoV-2 spike protein
2025 · Briefings in Bioinformatics
Tests whether a large language model can generate the S1 subunit of the SARS-CoV-2 spike protein.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Simone Marini hasn't set up this page yet. Their UF Scholars profile lists their publications.