Clinical Microbiologist → Infectious Disease Modeler → PhD Student in (Machine learning for) Epidemiology
I'm a PhD candidate (started April 2026) at the Helmholtz Centre for Infection Research, working on:
"Multi-Scale Determinants of Childhood Vaccine Response: Integrating Serological and Environmental Data"
Scope: My PhD investigates immunity gaps against vaccine-preventable diseases across the life course, and how biological, environmental, and sociodemographic determinants shape them — with the ultimate aim of informing targeted vaccination, catch-up, and booster strategies. The project has two empirical arms:
- 🧫 Vacc@EMVIC (LiNA cohort): longitudinal serological profiling of measles, mumps, rubella, and varicella antibody responses in children, linked to maternal immunization, early-life exposures (e.g. PFAS, air pollution), and breastfeeding — using mixed-effects models, causal inference, and interpretable machine learning.
- 📊 NAKO Gesundheitsstudie: record-based characterization of measles vaccination gaps in the German adult population, ahead of the Masernschutzgesetz.
Why this fits my path: my training in clinical microbiology grounds this work in the biological reality of infection and immune response, while my background in mathematical modeling of infectious diseases drives the analytical side — from mixed-effects trajectory models to exposure-mixture and machine-learning approaches for detecting heterogeneity in vaccine-induced protection. The PhD sits precisely at that intersection: using quantitative modeling to make sense of biologically and environmentally driven variability in immunity.
These are self-driven projects that extend my modeling background, mostly around agent-based models (ABMs) for infectious disease and hospital epidemiology.
| Project | Description | Status |
|---|---|---|
| Staph_Noso_Model | Agent-based model derived from my Master's/thesis work on nosocomial Staphylococcus transmission, built to serve as a reusable scaffold for future nosocomial transmission models in low- and middle-income country (LMIC) settings. | Active |
| EPI_ECS | A Python library for epidemic ABMs built on an Entity-Component-System (ECS) architecture, aimed at flexible, performant epidemic simulations. Primarily a learn-by-doing project — will be released once mature. | 🔧 In development |
| Hospi_ECS | An ECS-based ABM adapting and implementing procedural vector geography for hospital spatial modeling, used to simulate ward layouts and nosocomial transmission dynamics. | 🔧 In development |
Python · R · Mixed-effects models · Causal inference · Agent-Based Modeling · ECS architecture · Serology / immunoassay data · Epidemiological surveillance data
Open to conversations on vaccine immunology modeling, ABMs for hospital/nosocomial epidemiology, or ECS-based simulation design.
