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James is also a Co-Director of MACSI, the Mathematics Applications Consortium for Science and Industry whose aim is to foster new collaborative research, on problems that arise in science, engineering and industry, in order to produce world-class publications on mathematical modelling and produce world-class research and research training in mathematical modelling.
Professor Gleeson is a Principal Investigator with CONFIRM
• PhD in Applied Mathematics, Caltech University, USA
• MSc in Mathematical Physics, University College Dublin
• BSc in Mathematical Science, University College Dublin
• Member of Society for Industrial and Applied Mathematics: SIAM
• Member of European Complex Systems Society
James has extensive expertise in the areas of mathematical modelling and data analysis
As co-director of MACSI, Professor Gleeson is responsible for many industry-facing projects involving mathematical modelling and data analysis. Projects undertaken with industry include:
• Vistakon (Outcome: Increased efficiency and improved design of production lines)
• Boston Scientific (Outcome: Stent manufacturing process improved through modelling)
• Multiple Companies (Outcome: Quantitative criterion for the end of primary drying based on non-invasive measurements made in the freeze drier. Optimization of the lyophilisation process)
• Analog Devices (Outcome: Experimentation cost reduced through mathematical modelling of process)
• Rusal Alumina (Outcome: 200% increase in the accuracy of prediction of product quality. Model now employed on a daily basis.
• Stochastic Dynamics
• Contagion on complex networks
1. Gleeson JP and Durrett R, “Temporal profiles of avalanches on networks”, Nature Communication, 8, 1227 (2017) 2. Starnini M. et al. “Equivalence between non-Markovian and Markovian dynamics in epidemic spreading processes”, Physical Review Letters, 118, 12301 (2017) 3. Gleeson JP et al., “Competition-induced criticality in a model of meme popularity”, Physical Review Letters, 112, 048701 (2014) 4. Gleeson JP et al., “A simple generative model of collective online behaviour”, Proceedings of the National Academy of Sciences USA, 111, 10411 (2014). 5. Gleeson JP, “High-accuracy approximation of binary-state dynamics on networks”, Physical Review Letters, 107, 068701 (2011)