Machine Learning (CATS)

With the availability of massive amounts of data and computational power, what was once a fairly obscure affair has now acquired a certain notoriety. The huge number of algorithms used in machine learning turn out to be almost all increasingly subtle variations of four or five basic ideas, and these were the subject of this short course (10 CATS pts). Each of these ideas were introduced with an example, I then examined its simplest mathematical formulations and understood when it works, and why.

Integrative Neuroscience (MScR)

This research-based Masters program spanned all levels of modern cutting-edge Neuroscience (molecular, cellular, systems, regenerative, cognitive, clinical and computational) and provided me with a real-world project to work on within world-leading researchers at the Edinburgh Neuroscience community. I presented by work at the 2017 MIUA Conference and collaborated with Chang-Gung University in Taiwan.

Cognitive Neuroscience (BSc Hons)

This unique course placed a particular focus on brain-behaviour relationships, by bringing together evidence from molecular and cellular neuroscience, brain injury studies and neurological illness, while also looking at developmental and cognitive aspects. It explored a wide range of research-based and clinically relevant methodologies including brain imaging, computational modelling and comparative cognition (considering evolutionary and developmental approaches).

During my degree, I worked with Dr. Juha Silvanto on a final year Cognitive Neuroscience project on implicit memory. I received an 80% first class grade for the thesis, which was the highest in my cohort.