Choosing a course is one of the most important decisions you'll ever make! View our courses and see what our students and lecturers have to say about the courses you are interested in at the links below.
Each year more than 4,000 choose University of Galway as their University of choice. Find out what life at University of Galway is all about here.
About University of Galway
About University of Galway
Since 1845, University of Galway has been sharing the highest quality teaching and research with Ireland and the world. Find out what makes our University so special – from our distinguished history to the latest news and campus developments.
Colleges & Schools
Colleges & Schools
University of Galway has earned international recognition as a research-led university with a commitment to top quality teaching across a range of key areas of expertise.
- Research & Innovation
Business & Industry
Guiding Breakthrough Research at University of Galway
We explore and facilitate commercial opportunities for the research community at University of Galway, as well as facilitating industry partnership.
Alumni & Friends
Alumni & Friends
There are 128,000 University of Galway alumni worldwide. Stay connected to your alumni community! Join our social networks and update your details online.
At University of Galway, we believe that the best learning takes place when you apply what you learn in a real world context. That's why many of our courses include work placements or community projects.
Dr. Colm O'Riordan researches in the domains of information retrieval, evolutionary computation, artificial life and evolutionary game theory. He is also involved in a number of ongoing collaborative projects in graph theory, quantum games, information retrieval and mining from social media. He is willing to discuss potential project supervision in any of the above domains. For more detail on ongoing research please refer to publications list on http://www3.it.nuigalway.ie/cirg/publications.html
The project proposals give a flavour of some potential project areas.
Design of suitable weighting schemes for graph based representations in Information Retrieval
Traditional information retrieval approaches adopt a range of techniques to represent documents and users’ queries; these include sets, bags and vectors. The use of graph representations can help overcome some of the limitations of previous approaches (e.g. term independence assumption) and can help capture more intuitively and expressively the information in a document or a query. There have been a number of recent studies into the ’correctness’ of a weighting scheme using an axiomatic framework. This research will aim to adopt such a framework to graph representations, to derive a family of weighting schemes and to evaluate their performance on standard benchmarks.
Information Retrieval without queries
The traditional paradigm of users presenting queries to represent an information need has been the dominant model in information retrieval for decades. There is a growing realisation of the limitations of this approach and a move towards realising timely retrieval of relevant information in a manner that does not require explicit querying by the user. The aims of this research are to infer information needs on the users’ behalf based on the current task or upcoming tasks or meetings; to classify these needs (informational, transactional etc.) and to represent these needs as suitable queries; to retrieve information from collections (web, desktop) and to present these results to the user. The approaches and models will be evaluated for a range of task.
Complexity and difficulty in strategy games
This project will explore the relationship between the complexity of a game (search space, repetition, deceptive paths) and the notion of ’difficulty’ that a player may experience playing the game. The research will focus on a family of well defined strategy games (e.g. Mancala games) and will categorise a number of these in terms of complexity. Variants of the games with different levels of complexity will also be explored.
Random graph generation (co-supervised with James Cruickshank (Mathematics))
This project will involve researching approaches to generating random and small world graphs and analysing their properties. The project will involve considering dynamic properties of the graph during creation and will consider a number of approaches to developing the graphs.
In particular, various classes of random geometric graphs will be considered, including Euclidean and hyperbolic random graphs. The student will investigate the relationship between the underlying geometry and the dynamic properties of the graph.
Analysis and predication of growth in dynamic graphs on social media (co-supervised with Josephine Griffith)
The increased use of social media such as Facebook and Twitter has led to the growth of research into such graphs. One of the open questions is to predict the growth of such graphs and the spread of information through these graphs. In this work, graphs will be built based on interconnections between people (and clusters of people). Properties relating to the growth of such graphs will be captured. Data mining approaches will be used to predict new connections in the graph and also to predict what type of behaviour will lead to activity by other posters. In order to attempt to predict this will mine the relationships between graph properties, user activities and the resulting activities by other users.
Personalised user interaction in social media systems (co-supervised with Karen Young)
Retrieval and organisation of data and information from social media systems (e.g. Facebook and Twitter) is a very active area of research which poses several challenges to researchers and developers (scale of data, heterogeneity, real-time retrieval). This project will involve researching and developing suitable user interaction models based on users’ multiple needs. The goal of the project is to identify task at hand, users’ interests, users’ interaction style/model and to present information and paths to information based on those tasks and interaction styles.
Generating and visualising predictions in social media (co-supervised with Josephine Griffith)
With the richness of data available in social media, much focus has been placed on generating useful predications regarding data, places and people. This project will focus on two research questions – how best to generate a prediction based on the existing sources of evidence and secondly how best to explain the prediction in a visual manner. Analysis of user behaviour when given these explanations will be undertaken.
Strategy Choice in Quantum Evolutionary Game Theory (co supervised with Michael McGettrick (Mathematics))
Quantum Evolutionary Game Theory is a new field being developed at NUI, Galway that marries the ideas of Evolutionary Computation with Quantum Game Theory.
The main purpose of this PhD will be to investigate optimal strategies in quantum evolutionary games that are played on a network. As such, this is a model for calculating the behaviour of quantum agents. The expertise in quantum game theory comes from Michael Mc Gettrick, who has worked in quantum computation for a number of years. Colm O’Riordan provides the expertise in evolutionary computation, and also in classical game theory. The idea will be to model quantum evolutionary games played on some simple graphs (e.g. cycle, star graph,…) initially. Following from this, we anticipate results on how the behaviour depends on the adjacency of the nodes in the graph, and on its topology.
In our initial models, we also anticipate that the behaviour will depend on how we entangle the initial (quantum) state of the agents: Whether this entanglement is local (nearest neighbour, next nearest neighbour, etc.) or global.
For comparison with the classical case, the PhD student will examine the particular case of the quantum iterated prisoners dilemma. It is anticipated that this PhD work will result in theoretical advances important in the design of future quantum algorithms. The PhD student should be relatively comfortable coding with established packages (e.g. Mathematica, Maxima, Maple,…), or coding themselves directly in a high level programming language, in order to run simulations to check theoretical results.