Invited Talk by Sašo Džeroski on Mining Big and Complex Data on Mo 26.11.

It's our pleasure to announce a talk by Sašo Džeroski from the Jozef Stefan Institute, Slovenia, taking place on Monday 26th November 2018 at 4:30pm in C205.

Abstract

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 spatio-temporal 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, semi-supervised 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.

Short Bio

Sašo Džeroski is a scientific councillor 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 co-authored book, 4 co-edited 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).