Dr Vasileios Giagos
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Email
v.giagos@essex.ac.uk -
Location
6.303, Colchester Campus
Profile
Biography
The two major themes in my research career are stochastic modelling and Bayesian Analysis. In addition to these two major themes, I have an active research interest in the areas of Computational Statistics (MCMC, Particle Filters), Probability (SDEs), Machine Learning and AI with a very wide range of applications (e.g. Epidemiology, Biology, Health, Demographics, and Animal Science). I have been involved I am very interested in extending the scope of my research with collaborations with the industry and other disciplines.
Research and professional activities
Research interests
Inference for stochastic kinetic dynamics
Stochastic kinetic dynamics provide a modelling framework with a wide range of applications (e.g. Epidemiology, Population Dynamics). The development of inferential tools for the kinetic constants is particularly challenging and involves topics like the Bayesian Prior Specification, the development of efficient simulation algorithms (Markov Chain Monte Carlo, Sequential Monte Carlo, Approximate Bayesian Computation) and stochastic processes (Markov Jump Processes, Linear Noise Approximation, Stochastic Differential Equations).
Bayesian Analysis
I have an active research interest in the methodological, computational and applied aspects of Bayesian Analysis. Some examples include Hierarchical models, inference for stochastic processes, Approximate Bayesian Computation, Time Series, Hidden Markov Models, Imputation of Missing Data, and Empirical Bayes. Furthermore, these involve computational aspects like Markov Chain Monte Carlo methods, Particle Filters and Statistical Machine Learning. Finally, areas of applications involve epidemiology, Biology, Health, Animal Science, Demographics, road traffic.
Teaching and supervision
Current teaching responsibilities
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Databases and data processing with SQL (MA332)
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Data Visualisation (MA304)
Publications
Publications (1)
Fearnhead, P., Giagos, V. and Sherlock, C., (2012). Inference for reaction networks using the Linear Noise Approximation
Journal articles (4)
Jaggi, A., Drake, M., Siddiqui, E., Nazir, J., Giagos, V. and Fatoye, F., (2018). A critical appraisal of the principal guidelines for neurogenic lower urinary tract dysfunction using the AGREE II instrument. Neurourology and Urodynamics. 37 (8), 2945-2950
van den Berg, M., Giagos, V., Lee, C., Brown, WY. and Hinch, GN., (2016). Acceptance of novel food by horses: The influence of food cues and nutrient composition. Applied Animal Behaviour Science. 183, 59-67
van den Berg, M., Giagos, V., Lee, C., Brown, WY., Cawdell-Smith, AJ. and Hinch, GN., (2016). The influence of odour, taste and nutrients on feeding behaviour and food preferences in horses. Applied Animal Behaviour Science. 184, 41-50
Fearnhead, P., Giagos, V. and Sherlock, C., (2014). Inference for reaction networks using the linear noise approximation. Biometrics. 70 (2), 457-466
Conferences (1)
Wilcox, C., Djahel, S. and Giagos, V., (2021). Identifying the Main Causes of Medical Data Incompleteness in the Smart Healthcare Era
Grants and funding
2023
Croud Inc Ltd KTP Application
Innovate UK (formerly Technology Strategy Board)
Croud Inc Ltd KTP Application
Innovate UK (formerly Technology Strategy Board)