Ryan Forster

Develop Bayesian infectious disease models to project how treatment access and public health policies affect HIV and syphilis. Quantify uncertainty and population impacts, analyze claims data for treatment uptake, model Doxy-PEP effects across four U.S. cities, and communicate findings through publications and presentations.

Designed adversarial prompts and validated model-generated analyses using reproducible R workflows as ground truth comparisons.

Evaluated advanced large language models for factual accuracy, logical coherence, safety compliance, and methodological reliability. Worked with Meta.AI on curating live chat data for usability in RLHF training.

Conducted computational microbiology and genomic epidemiology using genomic, clinical, and public-health data. Built reproducible workflows for phylogenetics, microbial community analysis, and metabolic modeling. Applied machine learning, regression, Bayesian, time-series, and transmission models to study microbial function, SARS-CoV-2, surveillance, and patient outcomes.

Conducted translational TB, mycobacteriophage, and HSV research. Isolated and characterized phages, studied infection mechanisms, and trained junior researchers. Supported HSV vaccine preparation and safety testing for a Phase 1 trial with X-VAX Technology, phage-based TB diagnostics, and nanopore sequencing/bioinformatics workflows.

Isolated and characterized environmental mycobacteriophages using microbiological, molecular, and genomic methods. Performed bacterial culture, plaque assays, phage purification, DNA extraction, and genomic characterization while developing expertise in phage biology, host-virus interactions, biosafety, and pathogen identification.

Activities and societies: The Program Committee (PC) Poster Pick at CROI 2026 by Connie Celum on 'Projected Impact of Doxycycline PEP on Syphilis Incidence: Modeling Study in 4 US Cities'.

Postdoctoral Fellowship in Computational Epidemiology at Johns Hopkins Bloomberg School of Public Health.