Nick Antonopoulos

nick antonopoulos

Prof Nick Antonopoulos

Vice Principal of Research and Innovation

Biography

Nick joined Edinburgh Napier in January 2019 from the University of Derby, where he was Pro Vice Chancellor (Research and Innovation) and Research Professor in Cloud Computing.

He has more than 20 years of academic and leadership experience, with a very strong background in initiating, leading and delivering improvements at an institutional level.

Nick has an excellent international reputation in his field, evidenced by his papers, books, chairing of prestigious conferences, and his active leadership of broad reaching research partnerships nationally and internationally. He has an extensive network across government, commerce and industry.

As well as the Dean and Research and Innovation Office, the Deans of the Schools of Computing, Engineering & Built Environment and Health & Social Care report to Nick.

Date


35 results

An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence

Journal Article
Yuan, B., Panneerselvam, J., Liu, L., Antonopoulos, N., & Lu, Y. (2019)
An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence. IEEE Transactions on Industrial Informatics, 15(7), 4295-4305. https://doi.org/10.1109/tii.2019.2902877
Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge comp...

Cloud-based video analytics using convolutional neural networks

Journal Article
Yaseen, M. U., Anjum, A., Farid, M., & Antonopoulos, N. (2019)
Cloud-based video analytics using convolutional neural networks. Software: Practice and Experience, 49(4), 565-583. https://doi.org/10.1002/spe.2636
Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analyt...

Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds

Journal Article
Yaseen, M. U., Anjum, A., Rana, O., & Antonopoulos, N. (2019)
Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds. IEEE Transactions on Systems Man and Cybernetics: Systems, 49(1), 253-264. https://doi.org/10.1109/TSMC.2018.2840341
A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supportin...

An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity.

Journal Article
Panneerselvam, J., Liu, L., & Antonopoulos, N. (2018)
An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity. IEEE Transactions on Emerging Topics in Computing, https://doi.org/10.1109/TETC.2018.2794328
Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively du...

An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres

Journal Article
Panneerselvam, J., Liu, L., Lu, Y., & Antonopoulos, N. (2018)
An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres. Future Generation Computer Systems, 83, 239-249. https://doi.org/10.1016/j.future.2017.12.064
Cloud datacentre resources and the arriving jobs are addressed to be exhibiting increased level of heterogeneity. A single Cloud job may encompass one to several number of tas...

Modeling and analysis of a deep learning pipeline for cloud based video analytics

Conference Proceeding
Yaseen, M. U., Anjum, A., & Antonopoulos, N. (2017)
Modeling and analysis of a deep learning pipeline for cloud based video analytics. In BDCAT '17 Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, 121-130. https://doi.org/10.1145/3148055.3148081
Video analytics systems based on deep learning approaches are becoming the basis of many widespread applications including smart cities to aid people and traffic monitoring. T...

Big data analytics in healthcare: A cloud based framework for generating insights

Book Chapter
Anjum, A., Aizad, S., Arshad, B., Subhani, M. M., Davies-Tagg, D., Abdullah, T., & Antonopoulos, N. (2017)
Big data analytics in healthcare: A cloud based framework for generating insights. In N. Antonopoulos, & L. Gillam (Eds.), Cloud Computing, 153-170. Springer. https://doi.org/10.1007/978-3-319-54645-2_6
With exabytes of data being generated from genome sequencing, a whole new science behind genomics big data has emerged. As technology improves, the cost of sequencing a human ...

InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments

Journal Article
Panneerselvam, J., Liu, L., & Antonopoulos, N. (2018)
InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments. Future Generation Computer Systems, 80, 322-341. https://doi.org/10.1016/j.future.2017.05.022
Cloud Computing has emerged as a low cost anywhere anytime computing paradigm. Given the energy consumption characteristics of the Cloud resources, service providers are under...

Efficient service discovery in decentralized online social networks

Journal Article
Yuan, B., Liu, L., & Antonopoulos, N. (2018)
Efficient service discovery in decentralized online social networks. Future Generation Computer Systems, 86, 775-791. https://doi.org/10.1016/j.future.2017.04.022
Online social networks (OSN) have attracted millions of users worldwide over the last decade. There are a series of urgent issues faced by existing OSN such as information ove...

Clinical and genomics data integration using meta-dimensional approach

Conference Proceeding
Subhani, M. M., Anjum, A., Koop, A., & Antonopoulos, N. (2016)
Clinical and genomics data integration using meta-dimensional approach. In UCC '16 Proceedings of the 9th International Conference on Utility and Cloud Computinghttps://doi.org/10.1145/2996890.3007896
Clinical and genomics datasets contain humongous amount of information which are used in their respective environments independently to produce new science or better explain e...