Training

Teaching Interests

My teaching interests focus on two core areas that I consider essential to a well-rounded undergraduate science course: student-directed learning and transferable skills, particularly experimental design, data analysis and programming skills.

Teaching at the University of Glasgow

My undergraduate teaching at the University of Glasgow has included first year Physics lectures on the Frontiers of Physics, where first year students are exposed to current research at the university, and the development of a new second year practical Physics lab looking at polarisation and saccharimetry.

Teaching at Durham University

Before coming to Glasgow I taught at my previous university, Durham University. At Durham I taught a student-directed programming course, enabling first year Computer Science undergaduates to learn the Python programming language and develop their own abilities to find and explore resources that they’ll use throughout their course and probably careers.

My response answer to a question in the class is “What terms have you searched for?” as the internet is the modern resources for programmers and an ability to utilise resources such as Stack Overflow or your favourite search engine (mine is DuckDuckGo; try !so Python 3) I find is the key difference between an independent learner/researcher in later years and a student who’s just getting grades.

Resources

Here are links to a few resources that I’ve put together as part of my teaching duties.

  • Slides and samples codes for my talk to engineering and computing sciences PhD students: Harry Plotter and the Multiple Visualisation Softwares can be found on my GitHub account here.

Training Courses

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Bioimage analysis with Python

The aim of this 5 days course is to develop motivated participants toward becoming independent BioImage Analysts in an imaging facility or research role. Participants will be taught theory and algorithms relating to bioimage analysis using Python as the primary coding language. Lectures will focus on image analysis theory and applications. Topics to be covered include: Image Analysis and image processing, Python and Jupyter notebooks, Visualisation, Fiji to Python, Segmentation, Omero and Python, Image Registration, Colocalisation, Time-series analysis, Tracking, Machine Learning, and Applied Machine Learning.

Programming for Biologists

There’s no escaping it - computing is becoming an essential part of the scientist toolkit. From coping with the data deluge by automated analysis or simulating a mathematical model of your system, coding is an essential skill for the modern biologist. This hands-on, one-day course will introduce you to Python, a popular and powerful computer language. You will learn the basics of working with Python through the increasingly popular Jupyter Notebook system. But Don’t Panic - this course is designed for those with no existing coding experience.

Programming for Biologists

There’s no escaping it - computing is becoming an essential part of the scientist toolkit. From coping with the data deluge by automated analysis or simulating a mathematical model of your system, coding is an essential skill for the modern biologist. This hands-on, one-day course will introduce you to Python, a popular and powerful computer language. You will learn the basics of working with Python through the increasingly popular Jupyter Notebook system. But Don’t Panic - this course is designed for those with no existing coding experience.