Kostas’ research focuses on developing and applying advanced computational methods, such as Markov Chain and Sequential Monte Carlo, for Bayesian Inference. His methodology has mostly targeted continuous time probability models based on stochastic differential equations driven by standard or fractional Brownian motion. The areas of application include Financial and Econometric Time Series as well biomedical problems such as stochastic epidemic models and analysis of growth curves.
Prior to joining the Statistics Department of ÐÓ°ÉÂÛ̳, he was a post-doctoral researcher at the University of Cambridge, in the Signal Processing Laboratory of the Engineering Department. He completed his PhD (2007) in the Statistics Department of the Athens University of Economics and Business while spending some time at University of Lancaster.