Sašo Džeroski is Head of the Department of knowledge technologies at the Jozef Stefan Institute and full professor at the Jozef Stefan International Postgraduate School, both in Ljubljana, Slovenia. He is a fellow of EurAI, the European Association of AI, in recognition of his “Pioneering Work in the field of AI”. He is a member of the Macedonian Academy of Sciences and Arts and a member of Academia Europea. He is past president and current vice-president of SLAIS, the Slovenian Artificial Intelligence Society.
His research interests focus on explainable machine learning, computational scientific discovery, and semantic technologies, all in the context of artificial intelligence for science. His group has developed machine learning methods that learn explainable models from complex data in the presence of domain knowledge: These include methods for multi-target prediction, semi-supervised and relational learning, and learning from data streams, as well as automated modelling of dynamical systems. The developed methods, released in open-source software, have been used to solve important problems in science and society, including agriculture and environmental sciences, medicine and life sciences, physics and material sciences, and space operations/ Earth observation.
Professor Džeroski has lead (as coordinator) many national and international (EU-funded ) projects and has participated in many more. He currently leads a large national project titled “Artificial Intelligence for Science”. He is also the technical coordinator of the Slovenian Artificial Intelligence Factory. The work of professor Džeroski has been extensively published and is highly cited: With more than 26500 citations and an h-index of 76 (in the GoogleScholar database), prof. Džeroski is the most frequently cited computer scientist in Slovenia (according to the 2025 ranking by Research.com). He has supervised many PhD students (more than 30), as well as PostDoc fellows (more than 15), who are now active in both academia and industry in many countries across three continents.
In the context of SQUASH, he is interested in the intersection between and interactions of machine learning and quantum computing. One potential topic of collaboration concerns meta-learning for automated configuration of quantum machine learning algorithms. Other topics at the intersection of artificial intelligence and quantum sciences (e.g., machine learning for quantum materials) are also of interest.
