I would welcome people to contact me if they are interested in conducting PhD research in areas that I research. I prefer to hear from people who already have some idea of what research question(s)/topic they would like to investigate. Prospective PhD students may be interested in one of the following projects, or may wish to propose the area of research (see below). Either way, please do contact me using the contact details in my profile page.
Funding may be available for PhDs through the School of Computing, for good candidates - see here for details of our GTA funding scheme in Computing and here for a more general list of scholarships available through Kent for PhD study in Computing.
Information about becoming a postgraduate in the School of Computing is available here and course details and application links can be found here. See also this page for information about suggested PhD projects in the Artificial Intelligence and Data Analytics research group.
At application, we ask applicants to submit a research proposal outlining the research they want to undertake for their PhD. There are some useful links: here on writing a research proposal and also the relevant sections in Bundy et al’s Researchers Bible.
Computational creativity is the computational study of computer systems that can produce creative work or act creatively. Recent research has looked at how we can make such research more scientifically rigorous. In particular, how do we evaluate how creative a creative AI tool is?
Various contributions (including my own) have been made in terms of methodologies for evaluation of creative systems, however these methods focused on systems which generated at smaller scales. With LLMs now predominant in generative AI; the time is ripe for seeing how evaluative methods can be scaled up to cope with the sheer amount of output an LLM/transformer based tool can generate, especially given that the inner workings of LLMs are often opaque or inaccesible.
It is expected that the PhD work will result in clear recommendations to the creative AI research community about how to evaluate their systems, and contribute towards solving the issues of scale of output and opaqueness of models, not addressed by the existing methodologies that exist.
In this project the student will experiment with incorporating evaluation methods into a creative system and analyse the results to explore how computational creativity systems can incorporate self-evaluation. The creative systems studied could be in the area of musical or linguistic creativity, or in a creative area of the student’s choosing. It is up to the student to decide whether to focus on evaluation methods for evaluating the quality of output from a creative system or the creativity of the system itself (or both). The PhD applicant would be required to propose how they would will explore the above scenarios, for a more specific project. Anna is happy to guide students in this and help them develop their research proposal.
Traditionally, when computational software performs music the performances can be criticised for being too unnatural, lacking interpretation and, in short, being too mechanical. However much progress has been made within the field of expressive musical performance and musical interpretation expression. Alongside these advances have been interesting findings in musical expectation (i.e. what people expect to hear when listening to a piece of music), as well as work on emotions that are present within music and on how information and meaning are conveyed in music. Each of these advances raises questions of how the relevant aspects could be interpreted by a musical performer.
Potential application areas for computer systems that can perform music in an appropriately expressive manner include, for example, improving playback in music notation editors (like Sibelius), or the automated performance of music generated on-the-fly for 'hold' music (played when waiting on hold during phone calls). Practical work exploring this could involve writing software that performs existing pieces, or could be to write software that can improvise, interpreting incoming sound/music and generating an appropriate sonic/musical response to it in real time.
As well as the above suggested projects, I would welcome self-proposed research proposals in my research interests, particularly in: