Teaching

I have supervised more than seven students at both master and doctoral levels and been a Teaching Assistant for several modules in Machine Learning, Computer Vision, Programming, and Electronic Sensing at Imperial College London and Queen Mary University of London. My responsibilities included preparing teaching material, leading laboratory sessions, supporting students with exercises, and explaining theoretical concepts.

Below is a list of the modules I assisted, each with a short description.

Machine Learning for Neuroscience (Postgraduate) – Imperial College London

A postgraduate module focusing on neuroscience-inspired applications of machine learning and deep learning. I supported lectures and supervised practical sessions where students implemented key algorithms.
Material distributed on GitHub

Machine Learning (Postgraduate) – Queen Mary University of London

This module introduced core machine learning concepts including pattern recognition, clustering, regression, and neural networks. My teaching focused on guiding students through lab exercises and theoretical foundations.

Data Mining (Postgraduate) – Queen Mary University of London

A practice-oriented module covering data mining algorithms and their limitations. I supported students with hands-on implementation and exploration of real-world datasets.

Computer Programming (Postgraduate) – Queen Mary University of London

An introduction to programming principles and software design. Students learned to design and implement complete programs using structured approaches.

Electronic Sensing (Postgraduate) – Queen Mary University of London

A module covering sensing and instrumentation systems: signal theory, metrology, transduction, acquisition, conditioning, and system-level design. I assisted with exercises and supervised laboratory work on sensor data analysis.

Software Engineering (Undergraduate) – Queen Mary University of London

This module introduced the principles, tools, and methodologies required for developing and testing large-scale software systems.

Coding for Scientists (Undergraduate) – Queen Mary University of London

A practical introduction to programming for science students, primarily using Python. Teaching focused on problem-solving, computational thinking, and scientific scripting.