Dr Vasileios Giagos

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.

Data Science and Statistical Applications

Together with Prof Hongsheng Dai, Dr Jackie Wong and Dr Yanchun Bao, we are currently working with Croud Inc., supported by a two-year Knowledge Transfer Partnership involving the use of Bayesian Analysis in digital marketing. I am excited to explore research opportunities in data science, leveraging statistical methods to analyze complex datasets from diverse industries and domains, uncovering valuable insights, and proposing innovative solutions to support better-informed decisions.

Current research