



I am interested in the analysis and design of algorithms that extract actionable information from data and signals. Data driven inference involves learning mechanisms that relate data to the "world state" we are interested in, using mathematical frameworks from, for example, the theory of probability, optimisation and computational statistics.

Two important processing challenges that motivate my research are: i) the limited resources (e.g. computational budget, access to storage, communication bandwidth etc.) available to update a sufficiently complex model. ii) Complex environments by definition cannot be modelled by building upon naive assumptions. These models ought to be learned from and/or verified using previously recorded data which is a scarse resource. Tradeoffs in data size and model complexity is the second research theme I am interested in.

My research objective is to contribute to the understanding and methodological aspects of these challenges for general cybernetic systems.

The fields of study I am interested in are:
Statistical inference, signal processing and information fusion; Bayesian paradigms; Variational approaches, probabilistic
graphical models and message passing algorithms; Dynamic system models, population processes (e.g. Random finite sets);
Machine learning.

Please check
my publications each of which is built upon a combination of the above elements.




