Teaching

Workshop: Interactive data visualisation in Python 🐍

• Github repository
• Introduces attendees to the Plotly library in Python that enables quick interactive data visualisations
• Includes 4 Jupyter notebooks and requires about 5 hours
• Originally designed for the Centre for Mathematical Medicine, Fields Institute, Toronto and the Centre de Recherches Mathématiques, Montréal.

QLSC 600D1, Resetting and Entraining Biological Oscillators, McGill

• Course outline
• Bootcamp on nonlinear dynamics (slides)
• Early warning signals for bifurcations (slides, Jupyter notebook)
• Cardiac arrhythmia (slides)

Math 127, Calculus I for the Sciences, UWat

• Lecture notes (handwritten)

Math 117, Calculus I for Engineering, UWat

Exercises and solutions (LaTeX):

• Inequalities, Exponentials and Logarithms, Inverses
• Composite Functions, Piecewise Functions, Partial Fractions
• Trigonometric Functions, Hyperbolic Functions
• Limits and Continuity
• Differential Calculus
• Differentials, L’Hopital’s Rule, and Curve Sketching
• Riemann Integrals, The FTC and Integration Techniques
• Further Integration Techniques and Applications
• Improper Integrals, Polar Coordinates and Complex Numbers

AMATH 777, Stochastic Processes in the Physical Sciences, UWat

• Lecture on Gillespie's algorithm
• Notebook to implement Gillespie's algorithm (Mathematica)

Teaching pedagogy

I am interested in the development and implementation of cooperative learning techniques in the classroom. Below is an article I wrote as part of the Certificate in University Teaching at the Universtiy of Waterloo.

• On cooperative learning in undergraduate mathematics courses (2018)