Dr Massimo Cavallaro

I am a dedicated researcher and modeller with many interests, most of which can be placed under the umbrella of Life and Health Sciences. I specialize in developing and applying rigorous quantitative solutions, integrating simulations, machine learning, scientific computing, and statistical approaches. I advocate for the utilisation of predictive modelling approaches not only for risk and outcome prediction, but also for uncovering patterns of associations and providing mechanistic explanations.

Before joining Leicester I was Research Fellow at the University of Warwick, investigating mechanisms of mRNA transcription and then working at the Zeeman Institute for Systems Biology and Epidemiology research on health data. I have a PhD in Applied Mathematics, Queen Mary, University of London, London, UK, and an MSc in Theoretical Physics, Università degli Studi di Catania, Catania, Italy. 

My research interests also include Monte Carlo methods, explainable AI, and applications to natural and man-made systems. Some of the topics I work on are:

• Infectious disease modelling and public health,
• Non-equilibrium statistical mechanics and stochastic processes,
• Gene expression and transcription,
• Computational biology and medicine.

Please get in touch to explore research ideas and identify funding opportunities.

• Factors associated with cocaine use at 17 and 20 years old: a longitudinal analysis of a nationally representative cohort. M Brennan, M Cavallaro, D Mongan, A Doyle, S M L Zgaga, B Smyth, E Nixon, J-H Ivers, C Walsh, C McCrory, B Galvin, and N McCarthy, Journal of Adolescent Health, 2025.
• N-terminal tagging of RNA Polymerase II shapes transcriptomes more than C-terminal alterations. A Callan-Sidat, E Zewdu, M Cavallaro, J Liu, and D Hebenstreit, Iscience, 27 (6), 2024.
• Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis. M Cavallaro, L Dyson, MJ Tildesley, D Todkill, and MJ Keeling, Journal of the Royal Society. Interface, 20(208):20230410, 2023
• Bayesian inference of polymerase dynamics over the exclusion process. M Cavallaro, Y Wang, D Hebenstreit, R Dutta, Royal Society Open Science, 10 (8), 2023 (arXiv:2109.05100)
• The role of vaccination and public awareness in forecasts of Mpox incidence in the United Kingdom. SPC Brand, M Cavallaro, F Cumming, C Turner, I Florence, P Blomquist, J Hilton, L Guzman-Rincon, T House, JD Nokes, and MJ Keeling, Nature Communications, 14(1): 4011, 2023;
• Informing antimicrobial stewardship with explainable AI. M Cavallaro, Ed Moran, B Collyer, ND McCarthy, C Green, and MJ Keeling, PLOS Digital Health, 2(1): e0000162, 2023 (hdruk.ac.uk)
• Cluster detection with random neighbourhood covering: application to invasive Group A Streptococcal disease. M Cavallaro, J Coelho, D Ready, V Decraene, T Lamagni, ND McCarthy, D Todkill, and MJ Keeling, PLoS Computational Biology, 18(11):e1010726, 2022
• Timing RNA polymerase pausing with TV-PRO-seq. J Zhang, M Cavallaro, and D Hebenstreit, Cell Reports Methods, 1(6):100083, 2021
• 3'-5' crosstalk contributes to transcriptional bursting. M Cavallaro, MD Walsh, M Jones, J Teahan, S Tiberi, B Finkenstädt, and D Hebenstreit, Genome Biology, 22(1):56, 2021
• Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values. M Cavallaro, H Moiz, MJ Keeling, and ND McCarthy, PLoS Computational Biology, 17(6):e1009121, 2021
• Effective bandwidth of non-Markovian packet traffic. M Cavallaro and RJ Harris, Journal of Statistical Mechanics: Theory and Experiment, 2019(8):083404, 2019 (arXiv:1902.00477, lml.org.uk)
• Bayesian inference on stochastic gene transcription from flow cytometry data. S Tiberi, MD Walsh, M Cavallaro, D Hebenstreit, and B Finkenstädt, Bioinformatics, 34 (17):i647, 2018
• A framework for the direct evaluation of large deviations in non-Markovian processes. M Cavallaro and RJ Harris, Journal of Physics A: Mathematical and Theoretical, 49(47):47LT02, 2016 (arXiv:1603.05634)
• Temporally correlated zero-range process with open boundaries: steady state and fluctuations. M Cavallaro, RJ Mondragón, and RJ Harris, Physical Review E, 92(2):022137, 2015 (arXiv:1504.06309)
• Assessment of urban ecosystem resilience through hybrid social-physical complex networks. M Cavallaro, D Asprone, V Latora, G Manfredi, and V Nicosia, Computer-Aided Civil and Infrastructure Engineering, 29(8):608, 2014 (arXiv:1302.3263)
• Urban network resilience analysis in case of earthquakes. D Asprone, M Cavallaro, V Latora, G Manfredi, V Nicosia., In G Deodatis, BR Ellingwood, DM Frangopol, (eds.), Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2014 (pp. 4069-4075). London, CRC Press.