Learning and practice

Teaching

I teach computation as a way of reasoning—not merely as a collection of tools.

My teaching spans computational chemistry, scientific computing, data science and introductory computer science. The subjects differ, but the underlying aim is consistent: students should be able to move from a question to a defensible model, inspect the evidence and explain the limits of their conclusion.

How I approach a course

I combine conceptual explanation with guided practice. Students work with real or realistic data, make explicit choices, document their reasoning and learn to distinguish a technically successful output from a scientifically meaningful result.

Current interests

• Computational and theoretical chemistry
• Data and AI methods for scientists
• Scientific programming and reproducible research
• Project-based and studio-based learning
• Assessment that tests reasoning rather than recall alone

Course materials that are suitable for public use will be added here gradually.