جستجوی استاد راهنمای دانشگاهی

Aleš Leonardis is a Full Professor at the Faculty of Computer and Information Science, University of Ljubljana, serving as head of the Visual Cognitive Systems Laboratory. His academic career is anchored in computer science with a focus on advanced visual cognition systems. His research spans computer vision , deep learning , and cognitive systems engineering , emphasizing object recognition, motion analysis, and visual data inconsistency detection. Key projects include ARRS research programmes (2009-2024), European initiatives like POETICON and CogX, and national grants addressing mobile vision frameworks and large-scale object category learning in image databases. As laboratory head, he directs research on hierarchical compositional visual architectures and data-driven machine vision solutions through active projects like MV4.0 (2021-2024). His work bridges theoretical computer vision with practical applications in mobile computing and human-agent interaction systems.

• Computer Vision
• Deep Learning
• Machine Learning
• +۵ مورد دیگر

Nikola Ljubešić is an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana. He is a member of the Machine Learning and Language Technologies Laboratory (LKM Laboratory), where he contributes to research in computational linguistics and language technology development. Slovenian language resource creation NLP-focused machine learning Language technology infrastructure He is currently involved in the Slovenian Research Agency's program P6-0411 (2019-2024), focusing on language resources and technologies for the Slovenian language.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +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

Veljko Pejović is an Associate Professor at the Faculty of Computer and Information Science (FRI), University of Ljubljana, Slovenia, where he also serves as Head of the Computer Communications Laboratory. His research focuses on mobile computing with special emphasis on resource efficiency in mobile and IoT environments. His educational background includes a PhD in Computer Science from the University of California, Santa Barbara (2012) and a dipl. ing (BS) from the University of Belgrade, Serbia (2006). Pejović's research interests center around mobile deep learning, approximate computing, and resource-efficient computing. His work explores how computation accuracy can be dynamically adapted based on contextual factors to optimize resource usage without significantly compromising user experience. He has made significant contributions to mobile sensing, machine learning on resource-constrained devices, and security in IoT environments. His recent publications reveal a strong trend toward making AI more efficient and accessible on mobile and edge devices, with particular focus on approximate computing techniques, federated learning frameworks, and context-aware adaptation of computational resources. His research spans applications from precision agriculture using UAVs to behavioral authentication in IoT environments and mental health inference from mobile sensor data. Scientific Awards: 10-Year Impact Runner Up Award at ACM UbiComp for InterruptMe work Outstanding research achievement award for 2024 by the Faculty of Computer and Information Science Best Paper Nominee (top 4%) at UbiComp'14 for InterruptMe Pejović actively mentors PhD and master's students, with numerous theses resulting in workshop, conference, and journal publications. He serves as Associate Editor for ACM IMWUT and ACM JCSS, and has held organizational roles in major conferences including ACM UbiComp 2025. His research is supported by multiple significant projects including approXimation for adaptable diStributed artificial intelligence (ARIS), CODA, AgriAdapt, and CARMA. His laboratory, the Computer Communications Laboratory at FRI, focuses on developing practical systems and frameworks for resource-efficient mobile computing, with several open-source tools and datasets publicly available for the research community.

• Mobile deep learning
• Approximate computing
• Resource-efficient computing
• +۶ 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

Aleksander Sadikov is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, and Head of the Artificial Intelligence Laboratory. His work focuses on bridging theoretical and applied aspects of AI through interdisciplinary research. Research Interests: Spanning Artificial Intelligence and Deep Learning Intelligent Tutoring Systems Multi-Agent Systems Energy Poverty Forecasting Medical Imaging Applications Qualitative Modeling He leads the DRIFT project (real-time deep reinforcement learning for low-voltage networks) and contributes to initiatives like MAIBAI (metrological frameworks for AI image assessment). Laboratories & Affiliations: Head of Artificial Intelligence Laboratory Laboratory Member at Faculty of Computer and Information Science His projects include EU-funded PARENT (early diagnosis of neonatal impairments) and national ARRS grants in lung cancer biomarkers and regulatory sandboxes for AI.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ 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

Erik Štrumbelj is a researcher specializing in probability, statistics, and machine learning. His work focuses on stochastic processes, Monte Carlo methods, Bayesian statistics, and applications in sports analytics (player evaluation, match simulation, statistical forecasting). He has contributed to advancing computationally intensive statistical analysis and has collaborated on interdisciplinary projects involving data mining, high-performance computing, and explainable AI. Key research trends include: Developing game theory-based explanations for machine learning predictions Modeling sports outcomes using Markov processes Analyzing forecasting reliability through betting odds Integrating data mining with medical modeling He has been involved in numerous research projects funded by ARRS and international bilateral agreements, addressing topics like imbalanced data analysis, AI explainability, and hypercomputing applications.

• Probability
• Statistics
• Machine Learning
• +۵ مورد دیگر

Assistant Professor Jure żabkar is affiliated with the Faculty of Computer and Information Science at the University of Ljubljana, where he teaches Artificial Intelligence, Machine Learning, and Programming courses while serving as a Laboratory Member in the Artificial Intelligence Laboratory (Laboratorij LUI). His research focuses on applied artificial intelligence with emphasis on: Deep reinforcement learning for real-time optimization (e.g., low-voltage power grid management) Intelligent tutoring systems and qualitative modeling from data Medical applications including Parkinson's disease detection and lung cancer prognostics Multi-agent systems with incremental learning capabilities Current research includes the ARRS-funded DRIFT project (L2-4436, 2022-2025) on power distribution optimization. Past projects span European initiatives like X-MEDIA and XPERO, Slovenian ARRS programs (2009-2020), and Structural Funds projects for dyslexia screening (PKP6) and Parkinson's monitoring (PARKINSCHECK). As an active laboratory member, he contributes to AI systems development across domains from medical diagnostics to cultural heritage platforms, with recent work including smartphone ECG applications and knowledge-sharing frameworks.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Klen Čopič Pucihar serves as Associate Professor at the Faculty of Mathematics, Natural Sciences and Information Technologies (FAMNIT) at the University of Primorska in Koper, Slovenia. He holds multiple leadership roles including Department Chair of Information Sciences and Technologies, Deputy Chair of HICUP Lab, and Study Programme Coordinator for the Doctoral Computer Science program. His research centers on Human-Computer Interaction with particular emphasis on: Augmented, Mixed, and Virtual Reality systems Micro-gesture recognition using radar sensing (e.g., Google Soli) Paper interfaces and augmentation of physical media AR for educational applications, particularly vocabulary learning Addressing the 'dual-view problem' in handheld AR systems Dr. Čopič Pucihar's work is driven by the vision to 'unclog the bottleneck' between humans and digital information. His recent publications (2019-2025) show a strong trajectory from theoretical interaction techniques toward practical educational applications and novel input methods using radar sensing, with consistent output in top venues including CHI, ISS, MobileHCI, and ISMAR. His notable achievements include: Best Paper Award at ACM IUI 2025 Honorable Mentions at ACM EICS 2022 and ACM ISS 2022 Best Poster Award at ISMAR Multiple hackathon victories including at Columbia University As Study Programme Coordinator for Doctoral Computer Science, Dr. Čopič Pucihar mentors graduate students and shapes research directions. His HICUP Lab hosts an international research group focused on making digital interfaces more intuitive and effective for human use through advanced sensing methods and personalized services. HICUP Lab leverages techniques from data mining, machine learning, computer vision, and human perception to develop interfaces that function as extensions of human minds, bodies, and behavior, with the ultimate goal of enabling 'digital augmentation of human abilities to its fullest potential.'

• Augmented Reality
• Mixed Reality
• Virtual Reality
• +۷ مورد دیگر

Borut Werber serves as an Associate Professor at the Department of Informatics, University of Maribor, Slovenia, where he has maintained continuous academic affiliation since 2001. His professional profile combines rigorous academic work with external quality assessment roles through NAKVIS and EFQM. His educational foundation includes: Bachelor's degree in Organisation of Information Work (1990) Master of Science in Organising Information Systems (1994) PhD (2000) Professor Werber's research operates at the critical intersection of computer science and medical technology. His dual focus on subcutaneous microchips explores implantable device applications for medical monitoring and identification systems, while his AI skin cancer detection research develops machine learning algorithms for dermatological diagnostics. These complementary domains demonstrate his commitment to leveraging computational methods for tangible healthcare advancements, particularly in early disease detection and patient monitoring solutions. His external professional engagements as an assessor for national and European research evaluation bodies highlight his recognized expertise in academic quality assurance. The inclusion of his PADI diving instructor certification reveals a distinctive balance between technological innovation and practical skill development in unrelated domains.

• Artificial Intelligence
• Biomedical Engineering
• Medical Imaging
• +۳ مورد دیگر

Professor Boštjan Brumen serves as a Full Professor at the University of Maribor specializing in informatics with dual research foci in security/privacy and machine learning. His academic profile bridges technical informatics with tourism applications, particularly examining digitalization and security challenges in tourism contexts through interdisciplinary research. His research centers on password security and cryptoanalysis within security studies, while exploring algorithmic limitations in machine learning classification systems. Recently, he has integrated these domains with tourism research, investigating pandemic-related travel behavior, spatial sensitivity in urban tourism, and security vulnerabilities in tourism technology systems. This work appears in reputable journals including Computers & Security and Academica turistica. Professor Brumen teaches Data Analysis in Tourism across undergraduate, master's, and doctoral programs, employing a teaching methodology that prioritizes critical thinking and adaptability over factual memorization. His pedagogical approach combines lectures with discussion-based analysis followed by independent application exercises, emphasizing historical context and pattern recognition to prepare students for evolving technological landscapes. His publication record demonstrates consistent output in both technical informatics venues and tourism research journals, with recent 2020 publications spanning shoulder surfing experiments, city spatial sensitivity, and COVID-19 travel impact studies. While specific awards aren't documented, his work appears in high-impact journals including the Journal of Medical Internet Research (IF 4.7). Professor Brumen maintains active research supervision through course instruction and co-authored publications, though specific student names aren't listed. His interdisciplinary approach connects informatics fundamentals with practical tourism applications, suggesting ongoing institutional support for his research activities despite explicit grant details being unavailable.

• Security
• Privacy
• Machine Learning
• +۴ مورد دیگر