Teaching

Teaching Philosophy

I believe that good teaching is about making complex ideas accessible and inspiring curiosity. My approach emphasises:

  • Active learning – encouraging students to engage with problems hands-on rather than passively absorbing lectures.
  • Clarity – breaking down difficult concepts into intuitive building blocks.
  • Inclusivity – creating an environment where every student feels welcome and supported.
  • Practical skills – bridging theory and practice so that students can apply what they learn.

Courses

As Instructor

Course Level Semester Institution
Introduction to Machine Learning Undergraduate Spring 2024 University Name
Data Science Bootcamp Graduate Summer 2023 University Name

As Teaching Assistant

Course Instructor Semester Institution
Advanced Deep Learning Prof. X Fall 2023 University Name
Probability & Statistics Prof. Y Spring 2023 University Name
Algorithms & Data Structures Prof. Z Fall 2022 University Name

Tutorials & Workshops

Introduction to Quarto for Reproducible Research

Workshop, March 2024
A hands-on workshop introducing Quarto as a tool for building reproducible research documents, interactive notebooks, and websites — all from a single source.
[Slides]   [Materials]

Python for Data Science – Crash Course

Tutorial, November 2023
A two-hour crash course covering NumPy, pandas, Matplotlib, and scikit-learn, targeted at researchers transitioning to Python from MATLAB or R.
[Notebook]   [Recording]

Student Supervision

I have mentored undergraduate and graduate students on research and course projects. If you are a student looking for a project or mentorship, please reach out via the About page.

Student Level Topic Year
Student A Undergraduate Anomaly detection with autoencoders 2024
Student B Master’s Transfer learning for medical imaging 2023

Resources

Below are some resources I frequently recommend to students: