جست‌وجوی استاد و پژوهشگر

Lovro Šubeljمشاهده پروفایل

دانشیار

• Network Science
• Machine Learning with Graphs
• Data Analysis
• +۱۲ 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.

Igor Kononenkoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ 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.

Sergio Cabelloمشاهده پروفایل

استاد

• Discrete Algorithms
• Computational Geometry
• Combinatorial Optimization
• +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.

Saso Dzeroskiمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Computational Scientific Discovery
• +۷ 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 ).

• Computer Architecture
• Parallel Processing
• Computer Arithmetic
• +۵ مورد دیگر

Patricio Bulić is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. His primary research focuses on computer architecture, embedded systems, and approximate computing for energy-efficient hardware design. Research Areas: Computer Architecture, Parallel Processing, Embedded Systems, Approximate Computing Affiliation: Faculty of Computer and Information Science (University of Ljubljana) Laboratory: Laboratory for Adaptive Systems and Parallel Processing His recent publications explore logarithmic arithmetic and approximate computing techniques to optimize hardware performance while reducing power consumption. These methods have been applied in sensor networks, digital signal processing, and neural network implementations. Scientific Awards: Professor of the Year 2014 Professor of the Year 2020 Professor of the Year 2021 Professor of the Year 2022 He has led numerous research projects including the ARRS programme on Ubiquitous Computing and COST Action IC1303 for enhanced living environments. His teaching responsibilities include courses in Computer Systems Organization, Parallel and Distributed Systems, and Embedded Systems.

Miha Moškonمشاهده پروفایل

استاد

• Systems Biology
• Systems Medicine
• Synthetic Biology
• +۶ 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 .

Jurij Miheličمشاهده پروفایل

دانشیار

• Algorithms
• Discrete Mathematics
• Combinatorial Optimization
• +۸ مورد دیگر

Jurij Mihelič is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, and a member of the Laboratory for Algorithms and Data Structures where he conducts research in theoretical and applied computer science. His research interests include: Algorithms and discrete mathematics Combinatorial optimization Computational complexity Operating systems and system software Virtual machines and execution environments Algorithm engineering and experimental algorithmics Graph theory and network analysis Facility location and pattern matching Mihelič teaches courses in Operating Systems and Algorithms and Data Structures I. His research bridges theoretical computer science with practical applications, with a particular focus on developing efficient algorithms and system software solutions. His work spans both theoretical foundations and practical implementations, demonstrating a strong commitment to connecting academic research with real-world problems. His publications demonstrate consistent contributions to algorithm design, graph theory, and combinatorial optimization, showing a progression from purely theoretical work toward more applied research in recent years. Key themes include facility location problems, subgraph isomorphism algorithms, and educational tools for system software concepts. Mihelič has been actively involved in the academic community, serving on organizing committees for significant conferences including EuroCG 2015, LADS3 2014, and ALGO 2012. He is currently involved in multiple research projects, most notably the ARRS research program 'Parallel and distributed systems' (2020-2026), along with several other projects focused on graph optimization, big data, and system software development.

Nada Lavračمشاهده پروفایل

استاد

• machine learning
• data mining
• inductive logic programming
• +۵ 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).

Petra Kralj Novakمشاهده پروفایل

استادیار

• Social Media Mining
• Sentiment Analysis
• Network Analysis
• +۳ مورد دیگر

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.

Tomaž Curkمشاهده پروفایل

دانشیار

• Bioinformatics
• Machine Learning
• Data Science
• +۳ مورد دیگر

Assoc. Prof. Tomaž Curk is an Associate Professor and Vice-Dean at the Faculty of Computer and Information Science (UL FRI), University of Ljubljana. His research focuses on applying machine learning and bioinformatics to analyze large biological datasets, particularly in neurodegenerative diseases and systems biology. He teaches courses such as Introduction to Bioinformatics , Data Mining , and Bioinformatics Algorithms . He is a member of the Bioinformatics Laboratory and leads multiple research projects funded by ARRS (Slovenian Research Agency), EU, and structural funds. Notable projects include investigating RNA roles in liver cancer, posttranscriptional regulatory networks in neurodegenerative diseases, and applications of machine learning in systems biology. His work spans computational approaches to genomics, drug discovery, and combating antibiotic resistance. Prof. Curk’s office is located in room R3.08, and he holds office hours on Thursdays at 13:15 via Zoom or in person. His research integrates interdisciplinary methods, emphasizing data fusion and high-throughput genomic analysis.

• Parallel and Distributed Systems
• Graph Optimization
• Big Data
• +۷ مورد دیگر

Tomaž Dobravec is an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. He is actively involved in research and teaching in computer science, with a focus on system software, algorithms, and distributed computing. His work is supported by national research programmes and structural funds projects. His research interests include: Parallel and Distributed Systems Graph Optimization and Big Data Combinatorial Scientific Computing Mobile and Industrial Operating Systems Interactive Educational Software Development The trend in his research projects indicates a strong emphasis on algorithmic efficiency, large-scale data processing, and practical software solutions for both industrial and educational contexts. His involvement in long-term ARRS programmes (e.g., P2-0095) highlights sustained contributions to core computing disciplines. He has participated in several research and development projects, including: P2-0095 - Parallel and distributed systems (2020–2026) N2-0171 - Graph Theory and Combinatorial Scientific Computing (2021–2023) PKP6 - Mobile Operating System for Industrial Use (MobiIOS) (2020) Graph Optimisation and Big Data (2016–2019) Development of the Visual Assistant project (2016–2017) Design and programming of interactive electronic textbooks (2017) He teaches key undergraduate courses such as System Software Programming 2 , Algorithms and Data Structures 2 , and The C Programming Language , indicating a strong foundation in core computer science education. While no formal advisees are listed, his role in research projects suggests mentorship of students and junior researchers. There is no mention of scientific awards in the provided text.

Gašper Fele Žoržمشاهده پروفایل

استادیار

• Digital Forensics
• Network Security
• Algorithms and Data Structures

Assist. dr. Gašper Fele Žorž is a faculty member affiliated with IT Support Services. His teaching responsibilities include courses in Digital Forensics, Communications Protocols, and Network Security. Research Focus: Network security, digital forensics, and algorithmic structures. Projects: Contributed to structural funds projects (PKP 2) and European projects (HESUDI) focused on healthcare IT solutions, alongside leading an ARRS-funded research project on deep generative models for the beauty and fashion industry.

Tomaž Hočevarمشاهده پروفایل

استادیار

• Algorithms and Data Structures
• Machine Learning
• Network Analysis
• +۳ 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.

Igor Rožancمشاهده پروفایل

مدرس ارشد

• Software Engineering
• Algorithms
• Data Structures
• +۲ مورد دیگر

Senior Lecturer Igor Rožanc is affiliated with an academic institution, where he teaches Algorithms and Data Structures I , Testing and Quality , and Software Engineering . He serves as a Laboratory Member and Head of the Software Engineering Laboratory . Contact details include office hours on Fridays from 9-10 A.M. in Room R2.50 (Kabinet). His roles combine teaching responsibilities and laboratory leadership, focusing on software engineering principles and quality assurance practices.

Bojan Žunkovičمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۳ مورد دیگر

Bojan Žunkovič serves as an Assistant Professor at the University of Ljubljana, Faculty of Computer and Information Science, where he is affiliated with the Laboratory for Machine Learning and Language Technologies. His teaching portfolio includes Machine Learning, Algorithms and Data Structures 1, Topical Research Themes 2, and Selected Topics in Artificial Intelligence 2. His research spans critical domains in computational science: Machine Learning Artificial Intelligence Quantum Computing Natural Language Processing Tensor Networks Reservoir Computing Current investigations focus on quantum-classical machine learning integration, particularly through tensor network methodologies and NISQ-era quantum advantage exploration in reservoir computing systems. Dr. Žunkovič leads the ARRS-funded project J2-60034 (2025-2027) titled "Practical quantum advantage of reservoir computing on NISQ devices" and previously directed project J1-2480 (2020-2023) on "From classical to quantum machine learning through tensor networks". Based in laboratory room R2.26, he contributes to the Laboratory for Machine Learning and Language Technologies' mission of advancing AI and computational linguistics research through both theoretical frameworks and practical implementations.