Dr. Ajda Pretnar Žagar is a researcher at the University of Ljubljana's Faculty of Computer and Information Science and the Institute for Contemporary History. With a PhD in Anthropology from the University of Ljubljana (2021), she bridges computational methods with social sciences through interdisciplinary research. University of Ljubljana Institute for Contemporary History Her research focuses on: Quantitative Anthropology Computational Social Sciences Machine Learning Applications Algorithmic Futures Bioinformatics Recent projects include: REIMAGINE ADM (2022-2025): Reimagining public values in algorithmic futures DALI4US (2024-2026): Data Literacy for upper primary schools xAIM (2021-2024): Explainable AI in healthcare
Lovro Šubelj is an Associate Professor at the University of Ljubljana, Faculty of Computer and Information Science , specializing in Network Science and Machine Learning with Graphs . His work bridges theoretical and applied network analysis, with contributions to community detection, graph convexity, and network simplification. Research focuses on graph algorithms for network abstraction Teaching: MSc/PhD courses on Network Science, BSc programming courses Collaborates with institutions like Leiden University and Imperial College London Research Interests: His primary work explores structural and dynamic properties of complex networks, including community detection via label propagation, convex skeletons for network simplification, and intermediacy metrics for citation analysis. Secondary interests span data mining, stream mining, and statistical network modeling. Publication Trends: Recent work emphasizes scalable network algorithms (2023-2025), graph embeddings (2021), and geometric network properties like convexity (2018-2019). Earlier projects (2012-2017) addressed citation network analysis, social network fraud detection, and database consistency evaluation. Teaching & Outreach: Provides educational materials on YouTube, including courses on network science and programming. Maintains open-access data/code repositories through KONECT, ICON, and GitHub.
Jožef Stefan International Postgraduate SchoolSlovenia
Dr. Petra Kralj Novak is an Assistant Professor at the Department of Network and Data Science, Central European University (Vienna, Austria) and a part-time researcher at the Department of Knowledge Technologies, Jožef Stefan Institute (Ljubljana, Slovenia). She holds a PhD from Jožef Stefan International Postgraduate School, where her thesis on Supervised Descriptive Rule Induction was published in the Journal of Machine Learning Research. Her research focuses on social media mining , combining machine learning with network analysis to study sentiment, hate speech, and emoji semantics. She pioneered emoji sentiment analysis in her influential 2015 paper. Additional interests include knowledge discovery, subgroup mining, and applications in bioinformatics (e.g., GMO detection tools). Her publications predominantly explore machine learning applications in social networks (retweet dynamics, misinformation spread) and biomedical domains (gene expression, GMO testing). Recent work emphasizes social media analytics and network science, while earlier contributions centered on algorithmic methods for subgroup discovery and biomedical data mining. Leadership & Projects: Coordinator of EU REC AG project IMSyPP (2020-2022) on hate speech monitoring Member of DGCNECT expert group for 'Digital media and human well-being' Program co-chair for ECML PKDD 2022 and Discovery Science 2018 Teaching: Leads courses in Fundamentals of Data Analysis and Introduction to Machine Learning at Central European University.
Slavko Žitnik is an Associate Professor and Vice-dean at the Faculty of Computer and Information Science, University of Ljubljana, where he is a member of the Laboratory for Data Technologies. His academic career spans multiple research projects and international collaborations focusing on data technologies and natural language processing. His primary research interests include information retrieval, information extraction, natural language processing, entity extraction, relationship extraction, coreference resolution, data merging, redundancy elimination, and ontologies. Dr. Žitnik's work often bridges theoretical computer science with practical applications in various domains including education, healthcare, and smart city ecosystems. Dr. Žitnik has led and participated in numerous significant research projects including P2-0359 on Ubiquitous Computing (2023-2027), PoVeJMo on Adaptive Natural Language Processing with Large Language Models (2023-2026), and the GOBLIN COST Action for building global networks of large-scale knowledge graphs. His recent work demonstrates a strong focus on adapting natural language processing techniques with large language models and creating practical applications of these technologies. His scientific contributions span multiple domains including: Natural Language Processing and Information Extraction techniques Knowledge graph construction and integration Applications in education, healthcare, and smart city ecosystems Development of practical tools and systems for data processing Dr. Žitnik has established international collaborations with institutions including Harvard University's Department of Biomedical Informatics (where he conducted a research visit from July to October 2022), the University of South Florida, and various European partners through COST Actions and other collaborative frameworks.
