interaction with the BlueJ IDE. The data is collected without any further context: we do not know 
the age or experience of the programmer, whether they are on a course or not, whether they are doing 
well on their assignments, and so on. This allows for a large data set, but one that is stripped of useful 
context. This project would investigate collecting useful data (e.g. experience, course grades) for a 
subset of Blackbox participants, to provide a richer subset for other researchers, and to be used in the 
project to investigate associations between programming activity and success on a course. 
 
 
Data Science Strategies for Cancer Immunotherapy Application 
Supervisors: Dr Sophia Tsoka & Dr Grigorios Loukides 
 
Computational analysis of biomedical datasets can lead to understanding of disease systems and 
therapeutic interventions. We propose a project that will target the computational analysis of
informatics-phd-projects-2022-23