Nowadays problems are vastly more complex than anything we have tackled in the past. Such problems cannot be solved by simply applying methods which at an earlier stage might have been sufficient. If we want to solve them, we must revolutionise our thinking, revolutionise our methods and become beacons of knowledge to the younger generations.
Stella Kapodistria is assistant professor in the section Stochastics of the Department of Mathematics and Computer Science in Eindhoven University of Technology. Her main research interests are in applied probability and stochastic operations research, with a particular interest on the modelling of processes and on the decision making under uncertainty of practices arising in energy and maintenance. The research topics that motivate her research tend to be real-life problems arising from practical situations, which can be analyzed using traditional and non-traditional tools from stochastic processes and optimization. Her approach to education can be summed up as ‘engaging students, ensuring they have a full grasp of theoretical material and its practical relevance, and making sure they understand how this is applied.”
Stella received her BSc (2003), MSc (2006, Hons.), and PhD (2009, summa cum laude) in Mathematics from the University of Athens. She worked as a lecturer (2009-11) at the University of the Aegean. In 2011, she moved to the Netherlands and worked as a Postdoc at the Department of Mathematics and Computer Science, TU/e. Following this appointment, she held an assistant professor at Groningen University (2013-14). Since 2014, Stella is assistant professor at TU/e, in the Department of Mathematics and Computer Science in the Stochastic Operations Research group.Stella is a frequent guest speaker at events worldwide. She is on the editorial board of the Probability Engineering and Information Sciences journal and guest editor for the Annals of Operation Research journal.
Hidden Markov Models for wind farm power outputIEEE Transactions on Sustainable Energy (2018)
Short and long-term wind turbine power output predictionarXiv (2017)
The shorter queue polling modelAnnals of Operations Research (2016)
Approximate performance analysis of generalized join the shortest queue routing9th EAI International Conference on Performance Evaluation Methodologies and Tools (Valuetools 2015), December 14–16, 2015, Berlin, Germany (2016)
Matrix geometric approach for random walksStochastic Models (2017)