Krisztián Buza

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Krisztian Buza
assistant professor



chrisbuza (at) yahoo (dot) com
buza (at) INF (dot) ELTE (dot) HU
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Hungarian Scientific Bibliography (MTMT)
 

Short biography

Krisztian Buza is an assistant professor at the Eötvös Loránd University (ELTE).

He obtained his PhD from the University of Hildesheim (Germany) in 2011 for his work on time series classification.
He is a co-author of more than 40 publications, including the “best paper” of the IEEE Conference on Computational Science and Engineering (2010), while his joint work with Gabor Nagy was nominated for the Best Paper Award of the 12th Industrial Conference on Data Mining (Berlin, 2012). He has been an invited speaker of the 9th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications (2015) and gave an invited tutorial about time series classification at the 8th International Conference on Web Intelligence, Mining and Semantics (2018).

He served as reviewer or program committee member for various journals and conferences, including Neurocomputing, Knowledge-Based Systems, Pattern Recognition and Pacific-Asia Conference on Knowledge Discovery and Data Mining.

He participated in variuos research projects in cooperation with industrial partners such as Rolls Royce, Morgan Stanley and Capgemini.
He received the fellowship of the Warsaw Center of Mathematics and Computer Science (2013) and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences (2014). Between 2014 and 2017 he was the principal investigator of the “BioMining” project supported by the National Research, Development and Innovation Office.

His current research interests include data mining and machine learning with special focus on text mining, time-series classification, biomedical and industrial applications, such as drug-target interaction prediction, or classification of biomedical signals and brain imaging data.

Research interests: data mining in time series data, text mining, biomedical and industrial applications.

Contact

ELTE University - Faculity of Informatics
Data Science and Engineering Department


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Pázmány Péter str. 1/A
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