Biography
Dr. Barry Sheehan is a lecturer in risk and finance in the Kemmy Business School at the University of Limerick. He holds the course directorship role for a cluster of award-winning inter-disciplinary programmes, including the MSc in Machine Learning for Finance, the MSc. in Computational Finance, and the HCI awarded Grad. Dip. in Artificial Intelligence in Finance.
Dr. Sheehan is a researcher with significant insurance industry and academic experience. With a professional background in actuarial science, his research (indexed here) uses machine-learning techniques to estimate the changing risk profile produced by emerging technologies. He is a senior member of Emerging Risk Group (ERG) at the University of Limerick which has long-established expertise in insurance and risk management and has a continued success within large research consortia including a number of SFI, FP7, and EU H2020 research projects.
In particular, he contributed to the successful completion of three Horizon 2020 EU funded projects including PROTECT, Vision Inspired Driver Assistance Systems (VI-DAS) and Cloud Large Scale Video Analysis (Cloud-LSVA). These consortia are predominately driven by technical objectives and our role is one of assessing the legal, ethical, and actuarial impact of the state-of-the-art innovations.
Research Interests
Risk Management, Insurance, Machine Learning, Insurance, Quantitative Finance, Cybersecurity, Smart Mobility, Nanotechnology, Financial Regulation
Teaching Interests
Risk Analysis, Machine Learning Applications in Finance and Insurance, Risk and Insurance, Quantitative Finance
Publications
Book Chapters
Application of the data model: Pillar one
Heep-Altiner M.;Mullins M.;Rohlfs T.;Clasen F.;Gallinger G.;Gerlach M.;Keller V.;Loeken A.;Moor H.;Olbrich T.;Schwering J.;Sheehan B.
(2018)
Application of the data model: Pillar one
In Contributions to Management Science;
pp. 23-84
DOI: 10.1007/978-3-319-77060-4_2
Introduction
Heep-Altiner M.;Mullins M.;Rohlfs T.;Hintzen S.;Muders S.;Sheehan B.;Vennemann F.
(2018)
Introduction
In Contributions to Management Science;
pp. 1-21
DOI: 10.1007/978-3-319-77060-4_1
Peer Reviewed Journals
A quantitative bow-tie cyber risk classification and assessment framework
Barry Sheehan and Finbarr Murphy and Arash N. Kia and Ronan Kiely
(2021)
A quantitative bow-tie cyber risk classification and assessment framework
In Journal Of Risk Research;
pp. 1-20
DOI: 10.1080/13669877.2021.1900337
[ULIR]
Immune or at-risk? Stock markets and the significance of the COVID-19 pandemic.
O'Donnell N;Shannon D;Sheehan B;
(2021)
Immune or at-risk? Stock markets and the significance of the COVID-19 pandemic.
In Journal Of Behavioral And Experimental Finance;
DOI: 10.1016/j.jbef.2021.100477
[ULIR]
Regulatory and technical constraints: An overview of the technical possibilities and regulatory limitations of vehicle telematic data
McDonnell K.;Murphy F.;Sheehan B.;Masello L.;Castignani G.;Ryan C.
(2021)
Regulatory and technical constraints: An overview of the technical possibilities and regulatory limitations of vehicle telematic data
In Sensors;
DOI: 10.3390/s21103517
[ULIR]
Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach
Furxhi Irini and Arash Negahdari Kia and Darren Shannon and Tim Jannusch and Finbarr Murphy and Barry Sheehan
(2021)
Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach
In Array;
pp. 100075-
DOI: https://doi.org/10.1016/j.array.2021.100075
Connected and autonomous vehicles: A cyber-risk classification framework
Sheehan B.;Murphy F.;Mullins M.;Ryan C.
(2019)
Connected and autonomous vehicles: A cyber-risk classification framework
In Transportation Research Part A-Policy And Practice;
pp. 523-536
DOI: 10.1016/j.tra.2018.06.033
[ULIR]
Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics
Furxhi I.;Murphy F.;Poland C.;Sheehan B.;Mullins M.;Mantecca P.
(2019)
Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics
In Nanotoxicology;
DOI: 10.1080/17435390.2019.1595206
Hazard Screening Methods for Nanomaterials: A Comparative Study.
Sheehan, B.; Murphy, F.; Mullins, M.; Furxhi, I.; Costa, A.L.; Simeone, F.C.; Mantecca, P.
(2018)
Hazard Screening Methods for Nanomaterials: A Comparative Study.
In International Journal Of Molecular Sciences;
pp. 649-
DOI: 10.3390/ijms19030649
[ULIR]
Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach
Sheehan, B., Murphy, F., Ryan, C., Mullins, M. and Liu, H.Y
(2017)
Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach
In Transportation Research Part C-Emerging Technologies;
pp. 124-137
Application of Bayesian Networks for the Human Hazard Prediction and Safety Assessment of Nanomaterials
Hans J. P. Marvin, H.J.P., Bouzembrak, Y., Janssen, W.M., van der Zande, M., Murphy, F., Sheehan, B., Mullins, M., Stone, V., Bouwmeester, H.
(2017)
Application of Bayesian Networks for the Human Hazard Prediction and Safety Assessment of Nanomaterials
In Nanotoxicology;
A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
Murphy, F,Sheehan, B,Mullins, M,Bouwmeester, H,Marvin, HJP,Bouzembrak, Y,Costa, AL,Das, R,Stone, V,Tofail, SAM
(2016)
A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
In Nanoscale Research Letters;
DOI: 10.1186/s11671-016-1724-y
[ULIR]
Conference Publications
Proceedings of the IEEE Conference on Nanotechnology
Furxhi I.;Murphy F.;Sheehan B.;Mullins M.;Mantecca P.
(2019)
Proceedings of the IEEE Conference on Nanotechnology
DOI: 10.1109/NANO.2018.8626300
Bodyshop Event and Awards
Sheehan, B.
(2017)
Bodyshop Event and Awards
International Bodyshop Industry Symposium
Sheehan, B.
(2017)
International Bodyshop Industry Symposium
Intelligent Transport Systems (ITS) Ireland
Sheehan, B., Ryan, C. and Murphy, F.
(2016)
Intelligent Transport Systems (ITS) Ireland