We are focusing on multi-target prediction, developing various methods addressing different machine learning tasks. We are also addressing the topic of representation learning, where we are developing data mining methods for the analysis of heterogeneous data, and using them in several application domains.

Our long-term goal in the area of decision support is to develop methods and techniques of decision modeling, support them with software and integrate them with data mining systems. In the area of explainable artificial intelligence, we are developing and evaluating methods for variable importance estimation and feature ranking in several contexts.

We are addressing the fields of semantic technologies to support the process of data analysis, computational scientific discovery to propose the use of probabilistic grammars to represent domain knowledge in equation discovery, and computational creativity to develop new methods for the creative generation of natural language text.

We are working on machine learning methods for natural language processing and understanding. Our research is also on the field of text and network analytics is to combine methods of text mining, natural language processing, network analysis, and topic detection to reveal and highlight underlying characteristics in different domains. We are also leading CLARIN.SI, open access language resources and being active in the field of digital humanities.