Stella Kapodistria is associate professor in the section Stochastics of the Department of Mathematics and Computer Science in Eindhoven University of Technology. 

Stella Kapodistria’s research focuses on data-driven decision making and on the performance assessment of systems and networks under uncertainty. 

She creates algorithms that integrate real-time information to optimize system and network performance. Her work addresses the challenges of scalability and computational efficacy in complex systems and large networks. She is publishing papers in flagship peer-reviewed journals and conferences. She contributes to the academic community as an editorial board member of MCAP, previously serving on the editorial board of PEIS, and as a guest editor for ANOR. Stella has served on TPCs for prominent conferences like Sigmetrics, Performance, APS, ICORES, and QTNA. She has been instrumental in obtaining the funding, coordinating, and in setting up the 4TU Resilience Engineering center. She has been awarded (co-PI) an NWA-ORC, an NWO Big Data, and a TKI WoZ grant.

Collaboration with industrial partners in the Brainport area influenced her approach, which extends from purely fundamental research to applied research. These collaborations and numerous industrial projects have significantly shaped her vision and methodology in research.

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). She started on a tenurer track appintment in 2014  at TU/e, in the Department of Mathematics and Computer Science in the Stochastic Operations Research group. 

“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.”