Veranstalter: Knowledge Engineering Group – Fachbereich Informatik
Referent: Sašo Džeroski, Jozef Stefan Institute, Slovenia
Increasingly often, data mining has to learn predictive models from big data, which may have many examples or many input/output dimensions and may be streaming at very high rates. Contemporary predictive modeling problems may also be complex in a number of other ways: they may involve (a) structured data, both as input and output of the prediction process, (b) incompletely labelled data, and (c) data placed in a spatiotemporal or network context. The talk will first give an introduction to the different tasks encountered when learning from big and complex data. It will then present some methods for solving such tasks, focusing on structured output prediction, semisupervised learning (from incompletely annotated data), and learning from data streams. Finally, some illustrative applications of these methods will be described, ranging from genomics and medicine to image annotation and space exploration.
Sašo Džeroski is a scientific counselor at the Jozef Stefan Institute and a full professor at the Jozef Stefan International Postgraduate School. He leads a research group which develops methods for machine learning and data mining (including structured output prediction and automated modeling of dynamic systems) and investigates their use (in environmental sciences, incl. ecology/ecological modelling, and life sciences, incl. systems biology/systems medicine). His publication record includes 30 volumes (1 coauthored book, 4 coedited research monographs, 8 conference proceedings published with reputed publishers, 10 workshop proceedings and 7 journal special issues), more than 40 book chapters, more than 150 journal papers (more than 125 in journals with impact factors), and more than 300 conference papers.
He has participated in many international research projects and coordinated three of them in the past: Most recently, he lead the FET XTrack project MAESTRA (Learning from Massive, Incompletely annotated, and Structured Data). He has been scientific and/or organizational chair of numerous international conferences, including ECML PKDD 2017, DS-2014, MLSB-2009/10, ECEM and EAML-2004, ICML-1999 and ILP-1997/99. He became a fellow of EurAI, the European Association of Artificial Intelligence (formerly ECCAI) in 2008, in recognition for his “Pioneering Work in the field of AI and Outstanding Service for the European AI community”. In 2015, he was elected a foreign member of the Macedonian Academy of Sciences and Arts and in 2016 a member of Academia Europea (European Academy of Humanities, Letters and Sciences).