Aric Fowler
(he/they)
FHEA BSc (Hons)
Teaching Associate and PhD student
School of Computer Science and Informatics
Overview
I am a part time PhD student, currently in my third year. My research is on combinatorial optimisation, computational social choice, and measuring entertainment utility. My aim is to tackle questions such as "given a tournament schedule, which ordering of the rounds makes the tournament more likely to be exciting?" or "given a set of jury votes, which order should they be announced in to maximise audience entertainment?"
Outside of my research time, I work as a Teaching Associate helping to deliver modules on the Computer Science undergraduate courses. My specialisations are in automata theory, theoretical computer science, and combinatorial optimisation.
Publication
2024
- Fowler, A. and Booth, R. 2024. The score reveal problem: How do we maximise entertainment?. Presented at: 25th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2024), Kyoto, Japan, 18-24 November 2024.
Conferences
- Fowler, A. and Booth, R. 2024. The score reveal problem: How do we maximise entertainment?. Presented at: 25th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2024), Kyoto, Japan, 18-24 November 2024.
Research
My research concerns optimising tournaments and contests to improve their entertainment value. For example, when jury votes are announced in Eurovision what order should they be announced in to improve the drama of the contest? When sports tournaments are scheduled, what schedules are likely to be the most exciting? What constraints apply, to ensure the schedule is fair to all teams?
I am tackling the above questions using a formal definition of the Score Reveal Problem: Given a matrix of score assignments S, what permutation of the columns of S results in maximal entertainment? Many entertainment measures can be used to approach this problem, with different properties that may appeal to different audience types. For example, if the audience prefers a certain candidate we may want to maximise entertainment specifically based on that candidate.
Once we have defined a set of functions to measure entertainment, the immediate follow-up question is how to find the most entertaining permutation. Some functions are relatively easy to solve, with polynomial-time algorithms able to obtain optimal or near-optimal solutions most of the time. Does this indicate that these measures belong to P? Meanwhile, other measures seem to only be solvable using approximation methods such as combinatorial optimisation, perhaps indicating a higher level of hardness.
Teaching
I am a Teaching Associate in COMSC, primarily focusing on the mathematical and theoretical modules. I also supervise student projects relating to metaheuristic search and multi-objective optimisation.
Biography
I became a Teaching Associate at Cardiff University in January 2022 and started my PhD in optimisation and social choice shortly after. I achieved associate fellowship in 2023 followed by fellowship in 2024. I enjoy teaching, with previous experience as an Assistant Instructor with the Royal Lifesaving Society and experience volunteering to teach school children for a month in Lwang Ghalel, Nepal.
I have professional roots in forensic science (Foster & Freeman Ltd.) and cyber security (Logically Secure Ltd.)
Honours and awards
- Fellow (FHEA), 2024
- Associate Fellow (AFHEA), 2023
- Shortlisted for Cardiff PGR Tutor/Demonstrator of the Year 2023
- Nominated for COMSC Teaching Associate of the year 2023
- BSc (hons) Computer Science, 2021
- Performance Diploma in Classical Guitar (ARSM), 2018
- Performance Diploma in Classical Singing (DipLCM), 2018
- Survive & Save Distinction Award, 2016
Contact Details
Research themes
Specialisms
- Computational complexity and computability
- Computational Social Choice
- Combinatorial Optimisation