Assoc. Prof. Kostas Tsichlas

Assoc. Prof. Kostas Tsichlas


Collaborating Academic Faculty
Associate Professor in Algorithms and Computational Complexity in the Department of Computer Engineering & Informatics at the University of Patras


CV in Brief
I am an Associate Professor in Algorithms and Computational Complexity in the Department of Computer Engineering & Informatics at the University of Patras, a position I’ve held since 2020. Since 2023, I’ve also served as the director of GRCPC, the Greek chapter of the prestigious ICPC competitive programming competition. Previously, I was a faculty member in the School of Informatics at the Aristotle University of Thessaloniki (2008–2020). I have also been an Adjunct Professor at the Hellenic Open University from 2005 to 2014, and again since 2023. Over the years, I’ve held research positions at institutions abroad, including a sabbatical at MADALGO (Center for Massive Data Algorithmics) at Aarhus University, Denmark, in 2011, and a research assistant role in the Algorithm Design Group at King’s College London (2004–2005). I received my Ph.D. in 2004 and completed my military service in the Greek Army in 2003. My research focuses on the design and analysis of algorithms and data structures. I’ve worked extensively on fundamental data structures (such as dictionaries and priority queues) across various computational models (RAM, pointer machines, I/O model, distributed), as well as on computational geometry, string matching (with applications in bioinformatics and music analysis), indexing for databases, and combinatorial optimization in large-scale networks. Currently, I’m particularly interested in decentralized algorithms and the concept of emergence in complex algorithmic systems. Together with my Ph.D. students and collaborators, I also study virus propagation in networks, temporal network analysis, agent-based modeling, community detection, natural algorithms, and network-based notions of causality. Related to this, I explore how model-based and data-driven approaches—such as Physics-Informed Neural Networks—can be integrated. On the “applied” side, I’ve worked on the problem of energy disaggregation and the design of digital twins for energy systems.