Thomas Gorry, from Liverpool, is a third year PhD student in the University of Liverpool’s Department of Computer Science. He completed his BSc in Computer Science at the University in 2010 before progressing onto a MSc in Advanced Computer Science the following year. Thomas’ MSc project led him to work with his future supervisors, Professor Leszek Gąsieniec and Dr Russell Martin:
“Broadly, my PhD project concerns Algorithms for Distributed Computing. More specifically my research is concerned with Search, Discovery, Rendezvous, Gathering and Patrolling problems in both geometric and network based settings. The project aims to carry out research on the performance analysis of mobile agents or robots that find themselves in these scenarios.
“Due to the dynamic nature of our world, and beyond, it is important to model and find decentralised solutions to a variety of situations. Such models include varying not only the environment but also the capabilities of the agents, for example allowing for different maximum speeds, limited memory, the ability to leave markers or messages, as well as variations to communication limitations or methods.
“To vary the environment we would diversify the topologies in network settings, as well as differences in the geometric domains used. We use both deterministic and randomised approaches to solve these types of problems.
“The movement to decentralised self-organisation of robots has already begun, with companies using robot swarms to manage warehouses and development of swarm robotics to aid with disaster relief and evacuation.
“The desired outcome of my PhD is to augment this field by studying challenging situations, like limited communication or access to a global picture of the environment as well as the, previously rarely studied, aspect of multiple different robot top speeds across a range of environments and models. We also aim to see if any contributions can be made to this area by integrating knowledge from graph and network theory.
“As my PhD has progressed I have been devoting more of my time on new research areas in Social Network Analysis.
“One such area is bot detection in these networks. While analysing twitter networks we have noticed that, visually, it is easy to detect the pretence of bots. We believe that there should be an easy way to computationally locate these bot sub networks, and will be looking into this over the next year.
“I also work with a team based in the Department developing new methods of influence analysis and clustering methods within such networks. We have also developed software applications to aid this direction of our research.
“During the summer of 2013, I was involved in a joint six month project with the Institute of Integrative Biology at the University. This project was funded by an NERC grant for Efficient Biological Network Discovery and Analysis.
“The grant covered the analysis of functional genomics datasets of environmental influences. Through this grant I, together with my supervisors, were able to apply what we had learned from Social Network influence detection and clustering methods to biological networks as well.”
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