I am a microbiologist and epidemiologist working at the intersection of global health and AI.
I spent >16 years in international public health as epidemiologist and laboratory specialist with Médecins Sans Frontières, as a diagnostics senior technical officer with KNCV Tuberculosis Foundation, and as an independent consultant for USAID, FIND, and others.
My recent global health consulting work focuses on geospatial analysis and optimization of diagnostic networks, with country-level projects across Afghanistan, Bangladesh, Kenya, Mozambique, Namibia, Nigeria, the Philippines, Uganda, Vietnam, and Zimbabwe.
My heart is always with open-source work and thus, I developed and open-sourced the Diagnostic Network Explorer geospatial analysis app and published a multi-country accessibility analysis for novel TB diagnostics across 11 countries in East and Southern Africa.
I have recently trained in machine learning, neural networks, and the mathematical foundations of transformers, as well as applied AI systems including agentic workflows and knowledge management.
In my current AI research, I focus on how language models make decisions under uncertainty at the mechanistic level, i.e. how internal representations shape decision boundaries, how stored knowledge modulates evidence processing, and where these mechanisms break down or introduce bias. I use quantitative experimental frameworks from microbiology & epidemiology with white-box mechanistic interpretability tools to make these questions measurable.
I hold an MSc in Chemistry from the University of Marburg, a PhD in Natural Sciences from the ETH Zurich and a postgrad MSc in Public Health from the London School of Hygiene and Tropical Medicine.
More about me and my public health work -> Nosomics.org