Peter Andras
peter andras

Prof Peter Andras

Dean of School of Computing Engineering and the Built Environment

 0131 455 2456

  P.Andras@napier.ac.uk

  

Biography

Professor Peter Andras is the Dean of the Schools of Computing and Engineering & the Built Environment since August 2021.

Previously Peter was the Head of the School of Computing and Mathematics (2017 – 2021) and Professor of Computer Science and Informatics at Keele University from 2014 – 2021. Prior to this he worked at Newcastle University in the School of Computing (2002 – 2014) and the Department of Psychology (2000 – 2002).

He has a PhD in Mathematical Analysis of Artificial Neural Networks (2000), MSc in Artificial Intelligence (1996) and BSc in Computer Science (1995), all from the Babes-Bolyai University, Romania.

Peter’s research interests span a range of subjects including artificial intelligence, machine learning, complex systems, agent-based modelling, software engineering, systems theory, neuroscience, modelling and analysis of biological and social systems. He has worked on many research projects, mostly in collaboration with other researchers in computer science, psychology, chemistry, electronic engineering, mathematics, economics and other areas. His research projects have received around £2.5 million funding, his papers have been cited by over 2,400 times and his h-index is 25 according to Google Scholar.

Peter has extensive experience of working with industry, including several KTP projects and three university spin-out companies, one of which is on the London Stock Exchange since 2007 – eTherapeutics plc.

Peter is member of the Board of Governors of the International Neural Network Society (INNS), Fellow of the Royal Society of Biology, Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and member of the UK Computing Research Committee (UKCRC), IEEE Computer Society, Society for Artificial Intelligence and Simulation of Behaviour (AISB), International Society for Artificial Life (ISAL) and the Society for Neuroscience (SfN).

Peter serves on the EPSRC Peer Review College, the Royal Society International Exchanges Panel and the Royal Society APEX Awards Review College. He is also regularly serving as review panel member and project assessor for EU funding agencies.

Outside academia, Peter has an interest in politics and community affairs. He served as local councillor in Newcastle upon Tyne, parish councillor in Keele and stood in general elections for the Parliament. He has experience of working with and leading community organisations and leading a not-for-profit regional development consultancy and project management organisation.

School

Esteem

Grant Funding Panel Member

  • EPSRC grant panel member
  • EU Horizon 2020 / Horizon Europe / FP6 / FP7 grant panel member
  • Austria FIT IT grant panel member

 

Grant Reviewer

  • Leverhulme Trust grant reviewer
  • MRC grant reviewer
  • Austria FIT IT grant reviewer
  • BBSRC grant reviewer
  • EPSRC grant reviewer
  • EU Horizon 2020 / Horizon Europe / FP6 / FP7 grant reviewer

 

Filter by

Date


163 results

Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method

Journal Article
Yuan, H., Goh, K., Andras, P., Luo, W., Wang, C., & Gao, Y. (2024)
Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method. Transactions of the Institute of Measurement and Control, 46(3), 453-462. https://doi.org/10.1177/01423312231181993
This paper presents a new sensorless control method for four-wheel steering vehicles. Compared to the existing sensor-based control, this approach improved dynamic stability, ...

Federated Learning for Short-term Residential Load Forecasting

Journal Article
Briggs, C., Fan, Z., & Andras, P. (in press)
Federated Learning for Short-term Residential Load Forecasting. IEEE Open Access Journal of Power and Energy, https://doi.org/10.1109/oajpe.2022.3206220
Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply tra...

Scalability resilience framework using application-level fault injection for cloud-based software services

Journal Article
Al-Said Ahmad, A., & Andras, P. (2022)
Scalability resilience framework using application-level fault injection for cloud-based software services. Journal of cloud computing: advances, systems and applications, 11(1), Article 1. https://doi.org/10.1186/s13677-021-00277-z
This paper presents an investigation into the effect of faults on the scalability resilience of cloud-based software services. The study introduces an experimental framework u...

A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things

Book Chapter
Farhad, A., Woolley, S. I., & Andras, P. (2021)
A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things. In J. Mantas, L. Stoicu-Tivadar, C. Chronaki, A. Hasman, P. Weber, P. Gallos, …O. Sorina Chirila (Eds.), Public Health and Informatics (504-505). Amsterdam: IOS Press. https://doi.org/10.3233/SHTI210216
This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has ...

Compounding barriers to fairness in the digital technology ecosystem

Conference Proceeding
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021)
Compounding barriers to fairness in the digital technology ecosystem. In 2021 IEEE International Symposium on Technology and Society (ISTAS). https://doi.org/10.1109/istas52410.2021.9629166
A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are co...

A review of privacy-preserving federated learning for the Internet-of-Things

Book Chapter
Briggs, C., Fan, Z., & Andras, P. (2021)
A review of privacy-preserving federated learning for the Internet-of-Things. In M. Habib ur Rehman, & M. Medhat Gaber (Eds.), Federated Learning Systems: Towards Next-Generation AI (21-50). Cham: Springer. https://doi.org/10.1007/978-3-030-70604-3_2
The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is attributable to human activities and behavior. Collecting personal data and executing mach...

Federated Learning for Short-term Residential Energy Demand Forecasting

Working Paper
Briggs, C., Fan, Z., & Andras, P. (2021)
Federated Learning for Short-term Residential Energy Demand Forecasting
Energy demand forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As s...

Where do successful populations originate from?

Journal Article
Andras, P., & Stanton, A. (2021)
Where do successful populations originate from?. Journal of Theoretical Biology, 524, Article 110734. https://doi.org/10.1016/j.jtbi.2021.110734
In order to understand the dynamics of emergence and spreading of socio-technical innovations and population moves it is important to determine the place of origin of these po...

Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters

Conference Proceeding
Briggs, C., Fan, Z., & Andras, P. (2020)
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. In NeurIPS 2020 Workshop: Tackling Climate Change with Machine Learning
In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High res...

Federated learning with hierarchical clustering of local updates to improve training on non-IID data

Conference Proceeding
Briggs, C., Fan, Z., & Andras, P. (2020)
Federated learning with hierarchical clustering of local updates to improve training on non-IID data. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9207469
Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a ...

Current Post Grad projects