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Ivan BratkoView profile

Professor

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +5 more

Ivan Bratko is a Professor of Computer Science at the University of Ljubljana's Faculty of Computer and Information Science. He founded the Artificial Intelligence Laboratory in 1985 and served as its head until 2017, remaining an active member. Until 2002, he also directed the AI group at the Jožef Stefan Institute. His academic journey includes B.Sc., M.Sc., and Ph.D. degrees in electrical engineering and computer science, all from the University of Ljubljana. Bratko's research spans machine learning, knowledge-based systems, qualitative modeling, intelligent robotics, heuristic programming, and computer chess. His work focuses on learning from noisy data, combining learning with qualitative reasoning, constructive induction, Inductive Logic Programming, and applications in medicine and dynamic system control. He has authored over 200 scientific papers and influential books including Prolog Programming for Artificial Intelligence (third edition, 2001), KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998). His publication portfolio demonstrates consistent contributions to AI, with recent work emphasizing argument-based machine learning, qualitative modeling applications, and medical AI systems. These publications reveal strong interdisciplinary connections between theoretical AI and practical applications in environmental science, healthcare, and robotics. Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) Member of the Slovene Academy of Arts and Sciences (SAZU) Former editorial board member of Artificial Intelligence , Machine Learning , Journal of AI Research , and other leading journals Co-founder and first chairman of the Slovenian AI Society (SLAIS) Bratko has secured numerous research projects including ARRS programs on artificial intelligence (2009-2020), the PARKINSCHECK project for Parkinson's disease detection, and European projects like X-MEDIA and XPERO. His laboratory serves as the central hub for AI research at the University of Ljubljana, fostering collaborations across medical, environmental, and industrial domains. He has mentored numerous researchers and maintained active collaborations through visiting positions at institutions including Edinburgh University, University of New South Wales, and Delft University of Technology.

Igor KononenkoView profile

Professor

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +3 more

Prof. Igor Kononenko serves as a Professor at the Faculty of Computer and Information Science, University of Ljubljana, where he heads the Laboratory for Cognitive Modeling and teaches core courses including Algorithms and Data Structures 1, Artificial Intelligence, Intelligent Systems, and Machine Learning. Education: Ph.D. in Computer Science, University of Ljubljana (1990) Research Focus: His work centers on Artificial Intelligence , Machine Learning , and Cognitive Modeling , with recent emphasis on explainable AI and prediction reliability . He has pioneered techniques in feature contribution explanation, graph-based data mining, and archetypal analysis for complex datasets, resulting in approximately 210 publications and 10 textbooks . Publication Trends: Analysis of his 15 most recent articles reveals a decisive shift toward interpretable machine learning—particularly reliability estimation in data streams (2012-2014), graph mining for oceanographic/spatial data (2013-2019), and medical applications of explanation methods (2011-2018). His 2013-2016 work on archetypal analysis for multi-document summarization remains highly influential in NLP. Research Leadership: As principal investigator, he secured major funding including: Two ARRS programmes on Artificial Intelligence (2009-2014, 2015-2020) EU's AGROIT project for farming efficiency (2014-2016) Bilateral projects on imbalanced data learning (2010-2011), bioinformatics for cancer classification (2014-2015), and disease dataset analysis (2020-2021) Laboratory: The Laboratory for Cognitive Modeling under his direction drives innovation in AI theory and applications, with recent work spanning basketball analytics, coronary artery disease diagnostics, and hemodynamic simulation modeling.

Nada LavračView profile

Professor

• machine learning
• data mining
• inductive logic programming
• +5 more

Professor Nada Lavrač is a leading researcher at the Jožef Stefan Institute and Full Professor at the University of Nova Gorica , Slovenia. With over 40 years of experience in machine learning and data mining, she has made groundbreaking contributions to inductive logic programming, computational creativity, and biomedical data analysis. Her work spans applications in medicine, public health, bioinformatics, and industrial systems, with a focus on explainable AI and knowledge discovery. Head of ICT Programme at Jozef Stefan International Postgraduate School Principal researcher in EU projects H2020 FNS-Cloud and EMBEDDIA Author of 163-page monograph Representation Learning: Propositionalization and Embeddings (Springer, 2021) Research highlights include developing neuro-symbolic AI frameworks for conference scheduling, creating ontology-driven graph-based learning systems, and pioneering feature selection methods for high-dimensional biomedical datasets. Her work on tax2vec for taxonomy-based text classification has transformed short text analysis approaches. She has also led advancements in grapevine disease modeling using multi-omics data integration. Scientific accolades: 1984 national research excellence prize , 1997 Ambassador of Science of Slovenia , ECCAI Fellow (2007) , and Zois Recognition Award (2013) . Her citation metrics include 16,141 Google Scholar citations (h=54) and 4,622 WoS citations (h=31).

• Artificial Intelligence
• Machine Learning
• Data Visualization
• +5 more

Dr. Martin Možina is an Assistant Professor at the University of Ljubljana , affiliated with the Artificial Intelligence Laboratory since 2004. His work bridges classical machine learning with argumentation theory and focuses on interpretable AI methods. Research includes argument-based machine learning for knowledge-driven AI Developed nomogram visualization techniques for linear models Created automated chess tutors for move explanation Research projects span from 2004 to 2025, including: Current DRIFT project (2022-2025) on deep learning for power grid optimization Deep reinforcement learning applications in energy systems Argumentation for medical prognosis (lung cancer) and knowledge acquisition Publications show interdisciplinary work between: AI explainability (4/6 articles) Education technology (2/6) Healthcare AI applications (2/6) Graphical model interpretation (1/6) Multi-agent research (1/6) Teaching includes Decision Systems courses, with a focus on applied AI methods.