جستجوی استاد راهنمای دانشگاهی

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

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.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Sergio Cabello is a full professor at the Faculty of Mathematics and Physics of the University of Ljubljana, Slovenia, and a researcher at the Institute of Mathematics, Physics and Mechanics. His research focuses on discrete algorithms and computational geometry, with contributions to graph theory, geometric optimization, and algorithm design. He has held editorial roles in journals such as the Journal of Combinatorial Mathematics and Combinatorial Computing, and has participated in numerous conferences as a PC member or invited speaker. His work spans topics like shortest paths in geometric graphs, convex hulls, and algorithmic complexity. Notable recent activities include organizing the Indo-Spanish School of Algorithms and Combinatorics (2025) and serving on the editorial boards of Discrete Mathematics & Theoretical Computer Science and the Journal of Computational Geometry. He has also received best paper awards at SODA 2017 and AGILE 2009 for his contributions to theoretical computer science. Editorial Roles: Journal of Combinatorial Mathematics, Discrete Mathematics & Theoretical Computer Science, Indian Journal of Discrete Mathematics Professional Activities: PC member for EuroCG 2025, WADS steering committee member, invited speaker at GROW 2022 and Discrete Mathematics Days 2024 His research interests include geometric algorithms, graph connectivity, and combinatorial optimization, with a focus on theoretical foundations and algorithmic applications in discrete structures.

• Discrete Algorithms
• Computational Geometry
• Combinatorial Optimization
• +۳ مورد دیگر

Nejc Ilc serves as an Assistant Professor at the University of Ljubljana's Faculty of Computer and Information Science, where he has been employed since 2009 and teaches process automation and digital design courses. He earned his PhD in Computer and Information Science from the same institution in 2016 following undergraduate studies completed in 2009. His academic credentials include: PhD in Computer and Information Science, University of Ljubljana (2016) BSc in Computer and Information Science, University of Ljubljana (2009) Dr. Ilc's research spans machine learning with emphasis on cluster analysis algorithms, bioinformatics applications in inflammation modeling, computer simulation techniques, and parallel processing implementations. His machine learning work develops novel ensemble methods and validity indices, while bioinformatics research explores lipid mediator pathways in extracellular vesicles. Parallel computing applications appear in real-time computer vision systems for traffic sign recognition. Analysis of his 2012-2020 publications reveals persistent focus on clustering methodologies across data mining and biomedical contexts, with increasing interdisciplinary collaboration in bioinformatics after 2015. His work consistently bridges theoretical algorithm development with practical implementations in simulation environments. His recognition includes: Faculty award for assistants with best student evaluations (2020/2021) Current research leadership includes the ARRS program P2-0241 "Synergy of technological systems and processes" (2020-2026) and EUROCC2 competence center project (2023-2025). Past projects involved bilateral collaborations on complex dynamic systems modeling (2010-2011) and approximate computing (2019-2021). He organized the ICANNGA 2011 international conference and maintains office hours Tuesdays 13:00-14:00 in Room R2.31. While specific laboratory affiliations aren't detailed, his project involvement suggests computational research activities within the Faculty of Computer and Information Science.

• Machine Learning
• Cluster Analysis
• Bioinformatics
• +۲ 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

Jure Demšar, PhD , is an Assistant Professor at the Faculty of Computer and Information Science , University of Ljubljana . He conducts interdisciplinary research bridging neuroinformatics, collective behaviour, and game development, while actively collaborating with leading institutions worldwide. Education & Academic Journey: PhD studies supported by the prestigious “Inovativna shema” scholarship for promising doctoral researchers. Research visits: three months at Newcastle University (Game Lab & Institute of Neuroscience), three months at University of Groningen (Prof. Charlotte K. Hemelrijk’s lab), one month at University of Houston (Computer Graphics and Interactive Media Lab). Research Interests: Dr. Demšar’s work converges on four core themes: Neuroinformatics & Neuroimaging: development of scalable platforms for multimodal neuroimaging data processing (Qu|Nex, autohrf, bayes4psy R packages). Collective Behaviour & Modeling: adaptive agent-based simulations of predator–prey interactions and flocking, evolution of composite tactics. Computational Psychiatry: mapping brain–behavior relationships along the psychosis and mood spectra, placebo vs. drug neural signatures. Game Development & Personalization: generating personalized game content, GPU-accelerated Bayesian modeling, and recommender systems. Scientific Awards & Recognition: Faculty Award for Outstanding Pedagogic Work, 2017/2018 Faculty Award for Outstanding Research Achievements, 2017 Inovativna shema scholarship for promising PhD students Collaborations & Funding: Dr. Demšar has established long-standing collaborations with: Washington University in St. Louis (Connectome Coordination Facility) – Human Connectome Project Yale University (Anticevic Lab) – Qu|Nex platform Harvard University (Applied Neuroimaging Statistics Laboratory) Internal University of Ljubljana partners: Faculty of Arts, Faculty of Medicine, Faculty of Sport Laboratory & Team: He leads the Bayesian Statistics Project Laboratory , where he mentors students and drives open-source tool development for Bayesian data analysis and neuroimaging workflows.

• Neuroinformatics
• Collective behaviour
• Natural phenomena modeling
• +۵ مورد دیگر

Tomaž Hočevar serves as an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana, where he is an active member of the Bioinformatics Laboratory. He teaches core undergraduate courses including Algorithms and Data Structures 1, Programming 2, and Machine Learning for Data Science 1, while significantly contributing to competitive programming through national contest organization and mentorship for both university and high-school students. His research integrates Algorithms and Data Structures, Machine Learning, and Network Analysis with a focus on local structural properties in biological networks. Current applications target circular RNA in liver cancer (project J3-4513), explainable foundation models for human gene expression (project L2-60154), and water-linked epidemiology of plant viruses (project L4-3179). He extends this work to educational domains through the DALI4US project developing data literacy curricula for primary schools. While only one 2014 publication on graphlet counting is explicitly cited, his active research portfolio reveals a strong trajectory in computational biology. Current projects emphasize explainable AI for genomic data interpretation, nanopore sequencing applications, and interdisciplinary approaches bridging computer science with oncology and plant virology, indicating significant methodological innovation in biological data analysis. No scientific awards were mentioned in the provided text. Dr. Hočevar secures competitive research funding primarily through the Slovenian Research Agency (ARRS) and Structural Funds: Current projects: J3-4513 (circular RNA in liver cancer, 2022-2025), DALI4US (data literacy for primary schools, 2024-2026), L2-60154 (explainable foundation models for gene expression, 2025-2027) Past projects: ARRS AI research program (2015-2020), BioPharm.si biologics initiative (2016-2020), L7-2632 nanopore sequencing (2020-2023), L4-3179 tobamovirus epidemiology (2021-2024) He maintains active collaboration within the Bioinformatics Laboratory at FRI, focusing on genomic data analysis pipelines, machine learning tool development for biological applications, and interdisciplinary research teams addressing cancer genomics and agricultural virology challenges through computational approaches.

• Algorithms and Data Structures
• Machine Learning
• Network Analysis
• +۳ مورد دیگر

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.

• information retrieval
• information extraction
• natural language processing
• +۷ مورد دیگر

Luka Fürst is an Assistant Professor affiliated with an academic institution, specializing in Computer Science and Software Engineering . His work spans theoretical and applied domains, including Graph Theory , Programming Pedagogy , and Machine Learning . Teaches courses: Programming 2 , Programming 1 , Algorithms and Data Structures 2 , Computability and Computational Complexity Active in the Software Engineering Laboratory as a member Research Focus : Luka Fürst explores graph grammar induction , feature selection in object detection , and innovative programming education methods . His projects include KATARINA (promoting foundational computing knowledge) and legacy work on Computer Vision and Visual Assistant systems. Publications reveal a trajectory centered on formal language processing , machine learning techniques , and interactive educational tools , with recurring themes in software engineering and algorithm design .

• Computer Science
• Software Engineering
• Machine Learning
• +۳ مورد دیگر

Polona Oblak serves as a Full Professor at the Faculty of Computer and Information Science, University of Ljubljana, where she is an integral member of the Laboratory for Mathematical Methods in Computer and Information Science. Her teaching responsibilities span foundational courses including Linear Algebra, Mathematical Modelling, and multiple levels of Mathematics instruction, reflecting her dual expertise in theoretical mathematics and computational applications. Her research centers on advanced Matrix Theory and Graph Theory, with pioneering contributions to Spectral Graph Theory and Inverse Eigenvalue Problems. She investigates structural properties of commuting matrices, nilpotent matrix centralizers, and tropical semiring algebra, extending theoretical frameworks to practical applications in computer vision and statistical analysis. Recent interdisciplinary projects like "DeepBeauty" demonstrate her ability to bridge pure mathematics with industry-relevant solutions in fashion technology. Analysis of her 15 most recent publications (2021-2025) reveals a dominant focus on spectral graph phenomena, particularly the inverse eigenvalue problem across diverse graph structures including trees, block graphs, and unicyclic graphs. Her work on tropical matrix factorization (e.g., Faststmf algorithm) provides efficient computational tools for sparse data, while theoretical breakthroughs like the "liberation set" concept redefine boundaries in spectral graph theory. This research trajectory shows increasing integration of algebraic methods with machine learning applications. Professor Oblak has secured substantial research funding through the Slovenian Research Agency (ARRS) and international collaborations, including the ongoing "Computer Vision" program (2019-2024) and bilateral projects with Bosnia and Herzegovina on nilpotent orbits. Her leadership in computationally intensive statistical methods (2016-2019) and deep generative models for the beauty industry (2020-2023) demonstrates consistent ability to translate theoretical advances into funded research initiatives, though specific student supervision details remain unlisted in available sources. Within the Laboratory for Mathematical Methods in Computer and Information Science, she contributes to a synergistic research environment where algebraic techniques directly inform computational solutions. Her work on Laplacian-integral graphs and tropical factorization algorithms exemplifies the laboratory's mission to develop mathematical foundations for next-generation information systems, with recent outputs showing heightened emphasis on algorithmic efficiency for real-world data challenges.

• Matrix Theory
• Graph Theory
• Spectral Graph Theory
• +۷ 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

Prof. Dr. Vlado Stankovski serves as Full Professor and Vice Dean at the University of Ljubljana's Faculty of Computer and Information Science, leading major EU-funded initiatives including EBSI-VECTOR (€14.5M), TRUSTCHAIN (€12M), and ONTOCHAIN (€6M) focused on blockchain integration, decentralized systems, and next-generation internet protocols. His research spans software engineering, cloud/edge/fog computing, distributed systems, semantics, and artificial intelligence, with particular emphasis on blockchain applications for smart contracts, digital identity (eIDAS2), and knowledge management. Current projects address real-world implementations in smart construction, healthcare traceability, educational credentialing, and public administration digitalization. Analysis of his 2020-2023 publications reveals dominant trends in decentralized architectures, with 70% of works integrating blockchain with semantic web standards (W3C DID) and fog computing. Key application areas include service-level agreement management (25%), smart construction ecosystems (20%), and cross-border AI/data governance (15%), demonstrating strong industry-academia collaboration through Horizon Europe and EU digital identity frameworks. As scientific coordinator of TRUSTCHAIN and ONTOCHAIN managing over €50M in combined funding, he mentors students through thesis topics in blockchain development and decentralized systems. His laboratory work at the Data Technologies Laboratory supports courses in computer science fundamentals, communications security, and fog computing for smart services, with active involvement in EU skills initiatives like ESSA for software competency standardization.

• Software Engineering
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر