Teaching & mentoring

A flock of sheep moving through dust at sunset

I enjoy teaching the practical side of quantitative research: writing code others can trust, using version control, and bringing genomic data into epidemiological analyses.

Courses

  • 2025 – nowAdvanced Programming in EpidemiologyCo-instructor, University of Utah. Efficient and ethical use of AI in research, functional and object-oriented programming, debugging, profiling, C++ integration with R, and parallel computing. Course materials
  • 2025Integrating genomic data into hospital outbreak analysesShort course I developed and taught at the Workshop to Increase Diversity in Mathematical Modeling and Public Health, Harvard
  • 2024Git version control for graduate researchersCo-led, Harvard T.H. Chan School of Public Health
  • 2017 – 2021Medical statisticsCourses for medical and economics students, UMC Utrecht
  • 2013 – 2016StatisticsCourses for medical and economics students, RWTH Aachen

Mentoring

  • Mentored three undergraduate rotation students in tuberculosis epidemiology, University of Utah (2025–2026)
  • Led focus groups in pathogen genomics (University of Utah) and Bayesian statistics (Harvard)

Resources

Over the years I’ve collected books, papers, tutorials, and tools that helped me grow as a researcher. These pages organize them and pass them on. They lean toward infectious disease modeling, epidemiological methods, and the practical side of academic life, and they’re a work in progress. If you have suggestions, I’d love to hear from you.

Research practice
The “how” of doing science day to day: navigating AI tools thoughtfully, writing, giving talks, and staying organized.
Using AI in research
My personal, evolving guide to literature review and agentic coding workflows in computational epidemiology.
Methods reading list
Causal inference, Bayesian statistics, mathematical modeling, and coding.