Dr. Paul Doyle
Director of the Applied Social Computing Research group
Head of School of Computer Science, TU Dublin
Dr. Paul Doyle is Head of School of Computer Science at Technological University Dublin. He has spent over 20 years in industry in Silicon Valley, USA and Dublin, Ireland. Former Product and Quality Director for banking software company CR2, senior manager for Sun Microsystems, and senior developer for a BlueStar Financial Investment in Hong Kong.
An academia since 2008, he completed his PhD in the area of Astronomical Distributed Data Processing building a globally distributed data processing network Infrastructure. His research areas include Big Data processing, Distributed Systems, Systems Infrastructure, and educational pedagogy. In recognition of work done in China was recently awarded “top oversea professors 2020 for the Provence of HUNAN”.
Convene – Government of Ireland – Human Capital Initiative – €15,000,000
GETM3 – Horizon 2020 – €950,000
SAER: Early Career mentoring – Salesforce – €200,000
July Stimulus 2020 – Government of Ireland – €160,000
Global Labs (Python for Data Management) Public – MOOC 2021
Mobile Software (Blended Learning Distance Education) – BUCT and China Foreign Affairs Bureau
Global Innovation Teams – Erasmus+ – €300,000
SAP Computer Science Lab – €50,000
Applications in Progress
International Communications and Cultural Soft Power – National Social Science Foundation of China
HubLinked Knowledge Alliance – Erasmus+ – €1,000,000
Quality Blended Learning – Erasmus+ – €200,000
Global WorkIT – Erasmus+ – €200,000
- Member of the Institute of Big Data and Internet Innovation, Hunan University of Technology and Business, Changsha, Hunan, 410205, China
Visiting Lecturer at Kyungbook Natiional University South Korea
Visiting Lecturer at Bejing University of Chemical Technology, Beijing, China
Visiting Lecturer at Hunan Institute of Technology, Hunan, China
Doyle, P. et al. “Out of the Frying Pan … A Conversation. Changing Lives, Changing Careers?.” International Journal of HRD Practice, Policy and Research Vol 4.2 (2019): 121-125. doi: 10.22324/ijhrdppr.4.209.
Doyle, P. et. al. “High-Speed Distributed Data Process of Photometric Astronomical Data” 24 Apr 2019, Proceedings of the 2019 International Conference on Global Entrepreneurial Talent Management & Social Collaboration. Ko, I. Gwangju: Chonnam National University, pp. 22-32.
Doyle, P. et al. “HubLinked-A Global Curriculum Development Process.” (2019).
Lillis, D., Doyle P. et al. (2017) Global Software Innovators Strengthening the Software Innovation Capacity of Europe and Korea. The 2017 International Conference on Global Entrepreneurial Talent Management and Social Collaboration, Daegu, South Korea, July 2017.
Doyle, P. et al. “Significantly reducing the processing times of high-speed photometry data sets using a distributed computing model.” Software and Cyberinfrastructure for Astronomy II. Vol. 8451. International Society for Optics and Photonics, 2012.
O’Driscoll, C., Doyle, P. “The cost of security for a completely private ubiquitous environment.” 2010 International Conference for Internet Technology and Secured Transactions. IEEE, 2010.
Griffin, J., Doyle, P. “Desktop Virtualisation Scaling Experiments with VirtualBox.” The IT&T (2009): 97.
Doyle, P. et al. “Case studies in Thin Client acceptance.” ICIT Journal 3.1 (2009): 48-54.
Doyle, Paul, et al. “Evolution versus Revolution as a Strategy for Thin Client Acceptance: Case Study.” Evolution 2009 (2009): 07-01.
Doyle, P. Ubiquitous Desktops with Multi-factor Authentication. Digital Information Management, 2008 (ICDIM 2008): Third International Conference, London, 13-16 Nov. 2008, pp.198-203.
Doyle, P., Verbruggen, R. “Applying metrics to rule-based systems.” Proceedings Fourth International Conference on Software Engineering and Knowledge Engineering. IEEE, 1992.
Papers in review
South Korea, KNU: Doyle, P. et al “Applying Blending Learning and Flipped Learning Techniques in Korea using English: A Case Study” pending – submission to the International Journal of Education Technology in Higher Education. – Draft (2021).
China, BUCT: Jiang, Z., Doyle, P. et al. Text classification using novel term weighting schemes based on improved TF-IDF for processing Internet media reports – journal of Mathematic Problems in Engineering. In Press (2021).
China, Hunan University of Technology: Doyle, P., Tang, X. et al. NIMBUS: Astronomical Data Processing framework – Draft (2021).
UK, Northumbria University Newcastle: Valencia, A., Doyle, P. et al. “What can universities do for SMEs? The perspective of four different disciplines” Economic & Business Review. In Press (2021).
UK, Northumbria University Newcastle: Pearce, A., Doyle, P. et al. (2020). Managing a mega-project to explore and enhance careers: insights from Global Entrepreneurial Talent Management 3 in Murphy, W. & Tosti-Kharas, J. (Eds) Handbook of Research Methods in Careers, Boston: Edward Elgar Publishing. In Press (2021).
Doyle, P. “Building a scalable global data processing pipeline for large astronomical photometric datasets.” arXiv preprint arXiv:1502.02821 (2015).
Editorial Advisory Board
Special Edition, Global Entrepreneurial Talent Management challenges and opportunities for HRD, Volume, 4 Number 2, 2019 ISSN 2397-4583.
Pearce, A., Doyle, P. et al. (2020). Managing a mega-project to explore and enhance careers: insights from Global Entrepreneurial Talent Management 3 in Murphy, W. & Tosti-Kharas, J. (Eds) Handbook of Research Methods in Careers, Boston: Edward Elgar Publishing. In Press.
Harney, B., Doyle, P. et al. (2021). Developing Entrepreneurial Talent through Pedagogical Innovations:Insights from Three International Case studies, Organisation and Human Resource Management: An Educator’s Handbook, London: Routledge Publishing. In Press.
Harney, B., Doyle, P. et al. “Entrepreneurial Talent Management Development through Pedagogical Innovation: Three International Case Evaluations” Organization and Human Resource Management: An Educator’s Handbook – In Press.
Baloh, T., Gordon, D., Doyle, P. et al. “Quality Blended Learning Handbook”, Erasmus+ QBL Handbook for adult educators (2019).
Michael, R., Doyle, P. (2011). A Critical Performance Analysis of Thin Client Architectures. Lambert Academic Publishing (July 2011).
Astronomical Data Visualisation
Create a visually rich user experience for interaction with the virtual observatory and other astronomical data resources. Given the volumes of data, provide new interactive methods for viewing, searching, comparing and analysing data across the astronomical community which takes into account issues of accessibility to provide the data to a broader range of researchers who may be under-represented in astronomical science.
Distributed Astronomical Data Processing using Compute as a Service
Given the vast amounts of data generated in astronomy, distributed computing resources are required for researchers to process vast amounts of data. Amazon’s Lambda service is just one example of a compute as a service model which could be used to control processing costs for reseachers. Based on the distributed data processing pipeline NIMUS, this data processing pipeline would focus on sustainable distributed data processing using commodity cloud compute resources.
Transient Distributed Supercomputer Clouds using Mobile Devices
Science based data processing requirements have grown from terabytes to petabytes over the last 10 years, with exabyte sized data projects currently under development. The computing power required to process this volume of data primarily relies on high-end supercomputer clusters which are purpose built, expensive to maintain and often inflexible in terms of the service they provide. Measured in Teraflops, the required processing power of the most powerful parallel supercomputers will struggle to process this impending tsunami of scientific data. This research aims to investigate how a global data processing cloud can be constructed which horizontally scales using mobile devices to process scientific data at supercomputer processing rates. This distributed parallel processing supercomputer should be easily created and destroyed, allowing different types of scientific data to be processed as required. This distributed supercomputer would also incorporate security protocols, be resilient to individual node failure, and operate using an economic model which supported spot price bidding to control processing costs.
Real-time high-precision astronomical photometry data processing
Within the field of Photometric Astronomy, the measurement of stellar objects involves the recording of optical images using digital capture devices such as CCDs. Precise calibration of those images to remove source of noise, and the calculation of the object Flux over a period of time is required to create high precision light curves. This research is focused on the investigation of computational models designed to improve the precision of existing light curves generation as compared to existing processing techniques. The research will focus on automatic light curve generation, analysis of light curves and prioritisation of data processing using short integration imaging which is cognisant of atmospheric fluctuations. Machine Learning and image processing techniques are some of the techniques to be investigated to increase photometric precision. Additional components to this research will include a focus on overall speed of image processing and data analysis, allowing for large datasets to be processed in real-time, possible using multiple data sourced to increase the calibration of data. This research follows on directly from two recent PhDs in this area, distributed astronomical image processing for large data sets, and lucky photometry which improves light curve precision by considering atmospheric fluctuations. Ultimately the research should provide an insight into the precision of photometry that is possible from ground-based observatories and this in turn will inform limits to the potential scientific returns from ground-based infrastructures.