Mykola Pechenizkiy is a Full Professor at the department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), where he holds the Data Mining Chair. His research interests include data science, knowledge discovery and data mining, responsible analytics, including ethics/discrimination-awareness, context-aware predictive analytics, handling concept drift and reoccurring contexts, automation of feature construction and analytics on evolving networks.
His core expertise and research interests are in predictive analytics and knowledge discovery from evolving data, and in their application to real-world problems in industry, medicine and education. At the Data Science Center Eindhoven, he leads the Customer Journey interdisciplinary research program aiming at developing techniques for informed and responsible analytics.
Inspired by challenges of real-world applications, I develop the foundation for next generation predictive analytics.”
Mykola Pechenizkiy received his PhD from the Computer Science and Information Systems department at the University of Jyväskylä, Finland in 2005. In addition to his work at TU/e, he is an Adjunct Professor in Data Mining for Industrial Applications at the Department of Mathematical Information Technology the University of Jyväskylä. He has also been a visiting researcher at several universities, including Aalto University, University of Bournemouth, Columbia University, University of Cordoba, New York University, University Porto, University of Technology Sydney, Trinity College Dublin, and the University of Ulster.
Mykola has co-authored more than 100 peer-reviewed publications. He has been involved in organizing several successful conferences, thematic workshops and special issues with journals. He regularly serves on several program committees of leading data mining and AI conferences, including AAAI, IJCAI, ECMLPKDD, EDM, LAK, IDA, DSAA, DS, AISTATS, NIPS, SDM and editorial boards of DAMI, IEEE TLT and JEDM journals. He serves as the President of IEDMS, the International Educational Data Mining Society.
Recurring concept memory management in data streamsData Mining and Knowledge Discovery (2021)
Analyzing and repairing concept drift adaptation in data stream classificationMachine Learning (2022)
Exceptional spatio-temporal behavior mining through Bayesian non-parametric modelingData Mining and Knowledge Discovery (2020)
Mining exceptional relationships with grammar-guided genetic programmingKnowledge and Information Systems (2016)
A survey on concept drift adaptationACM Computing Surveys (2014)