جستجوی استاد راهنمای دانشگاهی

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.

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +۵ مورد دیگر

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.

• Network Science
• Machine Learning with Graphs
• Data Analysis
• +۱۲ مورد دیگر

Sašo Džeroski is a Professor at the Department of Knowledge Technologies, Jožef Stefan International Postgraduate School, and a Scientific Councillor at the Jožef Stefan Institute in Slovenia. He leads a research group of ~17 members, focusing on constraint-based data mining, structured output prediction, and automated modeling of dynamic systems, with applications in systems biology, ecology, and environmental sciences. His research interests span data mining, machine learning, computational scientific discovery, and their applications in life sciences and medicine. He has coordinated major projects like MAESTRA (FP7) and LANDMARK (H2020), and contributed to EU initiatives in systems biology ( E.E.T. Pipeline , PHAGOSYS ). He developed methodologies for predictive clustering trees and hierarchical multi-label classification, with software tools ProBMoT and CLUS . The 15 most recent articles highlight his work on ensemble learning for multi-output systems, structured output prediction in medical imaging, ecological modeling using machine learning, and ontologies for data mining. These publications reflect interdisciplinary trends combining computational methods with applications in fungal biology, endocytosis, and agricultural drainage. Scientific Awards: Foreign Member, Macedonian Academy of Sciences and Arts Advising includes mentoring PhD students (e.g., Nikola Simidjievski, Jovan Tanevski) and MSc students. Grants and projects emphasize EU funding (H2020, FP7) and national initiatives in knowledge technologies. He has served on editorial boards of journals like Machine Learning and Ecological Informatics , and organized key conferences (e.g., ECML PKDD-2015 ).

• Machine Learning
• Data Mining
• Computational Scientific Discovery
• +۷ مورد دیگر

Miha Moškon is a Full Professor at the University of Ljubljana, Faculty of Computer and Information Science, with a focus on computational models for biological systems in systems biology and systems medicine. His research includes context-specific genome-scale metabolic modeling, Boolean gene regulatory networks, and machine learning for complex datasets. Education: BSc (2007) and PhD (2012) in Computer Science from the University of Ljubljana. Research Interests: Synthetic biology, fuzzy logic, optimization, and computational design of biological systems, with applications in metabolic diseases and urban sustainability. Teaching: Courses in Computational Biology, Digital Logic Design, and Digital Circuits, alongside leading lab practices. Projects: Principal Investigator for ARRS J5-1798 (2019-2022), contributor to TranSYS H2020-MSCA-ITN (2019-2022), and coordinator for multiple Structural Funds projects. Scientific Awards: Coauthor of top research achievements (2022, 2017), Zois scholarship (1998-2008), iGEM prizes (2010, 2012, 2016), Prešeren's award (2007), and Dean's awards (2005, 2006). Editorial Roles: Member of the editorial boards for Scientific Reports , Neural Computing and Applications , and BMC Bioinformatics .

• Systems Biology
• Systems Medicine
• Synthetic Biology
• +۶ مورد دیگر

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).

• machine learning
• data mining
• inductive logic programming
• +۵ مورد دیگر

Prof. Marko Robnik Šikonja is a Full Professor at the Faculty of Computer and Information Science , University of Ljubljana . As head of the Machine Learning and Language Technologies Laboratory , he leads research in artificial intelligence, machine learning, data mining, natural language processing, and network analytics. He has authored over 150 publications with more than 5000 citations on Google Scholar. Research Focus: Deep neural networks, model explanation, embeddings, ensemble learning, and interdisciplinary applications in healthcare (e.g., STRATIFYHF for heart failure detection) and digital humanities (e.g., Language Resources for Slovene ). Scientific Awards: ECML/PKDD 2019 Journal Track Reviewer Award 2018 Outstanding Research Achievement at University of Ljubljana 2015 Golden Medal for contributions to the university Key Projects: STRATIFYHF (2023-2027): AI for heart failure risk stratification EMBEDDIA (2019-2021): Cross-lingual embeddings for European news KAUČ (2016-2022): Slovene textbook quality improvement

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر