Modeling how HIV and tuberculosis behave — cell by cell, patient by patient.
I'm Alexis Hoerter, PhD. I build agent-based and physiologically-based pharmacokinetic models of infectious disease, calibrate them against wet-lab and clinical data, and run them at scale on HPC. Lately, I pair that with LLM-assisted tooling to move faster without giving up rigor.
About
My work sits at the intersection of infectious disease biology, quantitative modeling, and data engineering. During my PhD and postdoc at Purdue's Weldon School of Biomedical Engineering, I built cellular-scale agent-based models of Mycobacterium tuberculosis, HIV, and their coinfection — calibrated against in vitro data from the Schlesinger lab (Texas Biomed) and pediatric immune activation datasets from the Indiana School of Medicine.
At Astellas Pharma, I brought the same toolkit to industry — using stochastic and ordinary differential equations on PBPK models to generate virtual patient populations that capture real-world variability.
I'm an advocate for thoughtful, guard-railed use of LLMs in scientific workflows: dashboards, data wrangling, and analysis scripting where speed matters and hallucinations don't.
What I work on
All research →Agent-based modeling
Cellular-scale simulations of granuloma formation, macrophage phagocytosis, T-cell proliferation, and pathogen dynamics.
PBPK & virtual patients
MATLAB / SimBiology models of drug distribution, extended with SDEs and Latin Hypercube Sampling to quantify variability.
AI-augmented analysis
Agentic coding loops for dashboards, data wrangling, and calibration workflows — with guardrails to keep results honest.