|Name: Imre Lendák
Position: Assistant Professor
E-mail: lendak (at) inf (dot) elte (dot) hu Links:
Imre Lendák is an assistant professor at the Eötvös Loránd University (ELTE). He obtained his PhD from the University of Novi Sad (Serbia) in 2011 for developing a data analysis algorithm capable to identify repetitive topologies in large network models of electric power distribution systems represented as mathematical graphs. His current research interests include applied security data science in critical infrastructures, crowd-sensing modeling & simulation, and graph visualization in Smart Grids. He was the publicity chair or general chair of the International Workshop on Crowd Assisted Sensing, Pervasive Systems and Communications (CASPer) between 2015 and 2018. Between 2012 and 2014 he was involved in two research projects at the Budapest University of Technology and Economics (BME – Hungary) focusing on various mobile crowd-sensing scenarios in the Smart City. In 2014 he received a grant from the Hungarian National Academy of Sciences for developing a simulation environment for analyzing a mobile crowd-sensing scenario based on the MASON multi-agent simulation engine. Currently he teaches Distributed Systems and Algorithms, as well as Information Security in Smart Grids. He coordinates one Erasmus+ Capacity Building in Higher Education (CBHE) project with the goal to develop different MSc and specialization programs in information security at four higher education institutions in Serbia. He is an IEEE and ACM member. He holds the IEEE’s Professional Software Engineering Master (PSEM) and Software Engineering Certified Instructor (SECI) certifications.
Research interests: Big data solutions, Text Mining.
A. Galloni, I. Lendák, T. Horváth: “A Novel Evaluation Metric for Synthetic Data Generation” . In: Yin, Hujun; Camacho, David; Novais, Paulo; Analide, Cesar (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2020 Springer International Publishing, (2020) pp. 25-34. Paper: Chapter 3. (link to article)
G. Szegedi, D. Bajdikné Veres, I. Lendák, T. Horváth: “Context-based Information Classification on Hungarian Invoices”. In: Martin, Holeňa; Tomáš, Horváth; Alica, Kelemenová; František, Mráz; Dana, Pardubská; Martin, Plátek; Petr, Sosík (ed.) Proceedings of the 20th Conference Information Technologies – Applications and Theory (ITAT 2020) CEUR Workshop Proceedings, (2020) pp. 147-151. (link to article)