Dr. Barry Sheehan is a lecturer in risk and finance in the Kemmy Business School at the University of Limerick. 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 currently contributing on three Horizon 2020 EU funded projects including PROTECT, Vision Inspired Driver Assistance Systems (VI-DAS) and Cloud Large Scale Video Analysis (Cloud-LSVA).
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 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 senior member of Emerging Risk Group (ERG) at the University of Limerick which has a long established expertise in insurance and risk management and has a continued success within large research consortia including a number of EU H2020 and FP7 research projects. Our team members are based across Europe and cover a wide array of research including machine learning, actuarial, insurance strategy, legal and ethical considerations of emerging technologies. We are members of two EU H2020 research projects related to semi-autonomous vehicles: VI-DAS (http://vi-das.eu/) and Cloud-LSVA (http://cloud-lsva.eu/). These consortia are predominately driven by technical objectives and our role is one of assessing the legal, ethical, and actuarial impact of ADAS technology.
Proceedings of the IEEE Conference on Nanotechnology
Furxhi I.;Murphy F.;Sheehan B.;Mullins M.;Mantecca P.
Bodyshop Event and Awards
International Bodyshop Industry Symposium
Intelligent Transport Systems (ITS) Ireland
Sheehan, B., Ryan, C. and Murphy, F.
Peer Reviewed Journal
Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics
Furxhi I.;Murphy F.;Poland C.;Sheehan B.;Mullins M.;Mantecca P.
Nanotoxicology DOI: 10.1080/17435390.2019.1595206
Connected and autonomous vehicles: A cyber-risk classification framework
Sheehan B.;Murphy F.;Mullins M.;Ryan C.
Transportation Research Part A-Policy And Practice DOI: 10.1016/j.tra.2018.06.033
Hazard Screening Methods for Nanomaterials: A Comparative Study.
Sheehan, B.; Murphy, F.; Mullins, M.; Furxhi, I.; Costa, A.L.; Simeone, F.C.; Mantecca, P.
International Journal Of Molecular Sciences DOI: 10.3390/ijms19030649
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.
Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach
Sheehan, B., Murphy, F., Ryan, C., Mullins, M. and Liu, H.Y
Transportation Research Part C-Emerging Technologies
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
Nanoscale Research Letters DOI: 10.1186/s11671-016-1724-y