Structured learning environments
Interactive Course Companions
Lecture-grounded learning paths for ecology and population genetics, with guided practice, progress tracking, and optional AI-supported feedback.
Teaching · Mentoring · Scientific learning
Learning biology by doing biology
Interactive, lecture-grounded resources that help students move from biological concepts to quantitative reasoning, data analysis, and defensible scientific interpretation.
How I teach biology
I design and lead practical exercises in population genetics, quantitative biology, and population and ecological genomics for university students. These courses combine short conceptual lectures, guided tutorials, hands-on data analysis, and interactive apps to help students investigate processes such as Hardy-Weinberg equilibrium, \(F_{ST}\) and genetic drift, natural selection, population structure, and the interpretation of genomic data.
My teaching is organised around authentic scientific decisions. Students are asked not only to run an analysis, but to identify the biological question, examine assumptions, evaluate uncertainty, and explain what the result does - and does not - support. Reproducible code, clear figures, and scientific language are treated as part of the analysis rather than as final decoration.
Beyond formal coursework, I supervise Bachelor’s and Master’s theses and mentor students individually. This includes support with research design, data management, statistical analysis, reproducible workflows, biological interpretation, scientific writing, and oral presentation, with the aim of developing independent and creative scientific thinking.
Interactive learning tools
Explore processes dynamically
Interactive Apps
Browser-based simulations for Hardy-Weinberg equilibrium, genetic drift, population differentiation, coalescence, and climate-related processes.
Reproducible practical work
Hands-on Genomics Tutorials
Stepwise practical material covering sequence quality control, mapping, variant workflows, command-line tools, population-genomic analysis, and interpretation.
Lecture-aligned practice
Companion Exercises
Focused exercises designed to consolidate lecture concepts, calculations, model interpretation, and biological reasoning.
Principles behind the tools
Concept before calculation
Students first identify the biological question, assumptions, and expected evidence, then apply equations or software.
Interaction before memorisation
Simulations make stochastic and multivariable processes visible, testable, and open to experimentation.
Evidence before conclusion
Analytical output is interpreted with uncertainty, alternative explanations, and appropriate scientific restraint.
Feedback that preserves agency
AI can support practice and explanation, while approved course sources and instructor decisions remain authoritative.
From concepts to independent analysis
1UnderstandConcepts, assumptions, and biological questions
2ExploreSimulations, examples, and guided calculations
3AnalyseRealistic data, reproducible code, and diagnostics
4InterpretUncertainty, alternatives, and scientific communication