Affiliation: Jožef Stefan Institute, SI
Keywords: Explainable AI, Machine learning, Structured data, Ensemble methods, Feature engineering, Feature ranking, Feature selection, Multi-target prediction
Full profile:
Dragi Kocev is a senior researcher at the Department of Knowledge Technologies, JSI. He completed his PhD in 2011 at the JSI Postgraduate School in Ljubljana on the topic of learning ensemble models for predicting structured outputs. He was a visiting research fellow at the University of Bari, Italy in 2014/2015. He has participated in several national Slovenian projects, the EU funded projects, and was co-coordinator of the FP7 FET Open project MAESTRA. He is currently the principal investigator of two ESA funded projects: GALAXAI – Machine learning for spacecraft operation and AiTLAS – AI prototyping environment for EO.
His research interests are in the field of explainable AI and includes the study, development and deployment of machine learning methods as well as their inclusion along the complete data life cycle. More specifically, his current research is aimed towards development of efficient methods for learning explainable models from data with structured outputs (e.g., predicting multiple targets, hierarchical multi-label classification) and their applications in life sciences, environmental sciences and engineering.