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. 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
Dr. Barry Sheehan investigates novel risk metrication and machine learning methodologies in the context of insurance and finance, attentive to a changing private and public emerging risk environment. He is a researcher with significant insurance industry and academic experience. With a professional background in actuarial science, his research 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.
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
Peer Reviewed Journals
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]
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
Other Publications
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
