جستجوی استاد راهنمای دانشگاهی

Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Blaž Zupan is a Full Professor at the University of Ljubljana, also affiliated with Baylor College of Medicine in Houston, teaching artificial intelligence and machine learning. He leads a fifteen-member bioinformatics laboratory and has authored over one hundred publications with more than ten thousand citations, reflecting significant impact in computational sciences. His research centers on explainable AI, machine learning, and data visualization, with deep applications in bioinformatics. He pioneered the Orange data mining suite—a visual programming tool for accessible analytics—and bioinformatics platforms like dictyExpress for gene expression analysis and GenePath for genetic network discovery, emphasizing user-friendly interfaces and real-world usability. Analysis of his recent publications reveals a trajectory from foundational AI (e.g., concept hierarchies in 1999) to contemporary bioinformatics, with current work focusing on single-cell genomics, batch-effect correction, and democratizing AI through visual programming. His tools bridge computational methods and biological discovery, particularly in systems biology and genomics. Zupan’s accolades include the Zois Award (2010), two University of Ljubljana Golden Plaques (2011, 2019), a Fulbright Scholarship (2013), and six student-voted Best Teacher Awards (2008–2017). He was named among the Top 100 Most Influential Innovators of Central and Eastern Europe (2016) by Res Publica, Financial Times, and Google. He secures substantial research funding, currently leading projects like KATARINA (computer science education for all), DALI4US (data literacy for primary schools), DANIO-ReCODE (genomics of regeneration), and L2-60154 (explainable AI for gene expression). Past projects span AI systems (2009–2020), drug discovery, and systems biology, demonstrating sustained interdisciplinary impact. As head of the Bioinformatics Laboratory, he fosters collaboration between computer science and biology, developing open-source tools that empower researchers globally. His consulting for industry and public organizations extends his influence into data-driven decision support and AI training.

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۴ مورد دیگر

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

• Computational Anthropology
• Machine Learning Applications in Social Sciences
• Data Mining
• +۳ مورد دیگر

Franc Solina is a Full Professor at the Faculty of Computer and Information Science , University of Ljubljana, Slovenia. He holds a Ph.D. from the University of Pennsylvania and has been active in academia since the 1990s. Education: Dipl.Ing. (1979) and M.S. (1982) in Electrical Engineering, University of Ljubljana Ph.D. (1987) in Computer and Information Science, University of Pennsylvania Research Interests: His work spans Computer Vision (superquadric modeling, underwater archaeology documentation), Human-Computer Interaction (interactive art installations, sign language technology), and Air Transport Applications (machine learning for aircraft trajectory prediction). He also explores Color-Based Applications and Visual Expression in art-technology intersections. Publication Trends: Franc's 15 most recent articles focus on superquadric modeling (1990-2004), underwater archaeology (2013-2015), and machine learning applications (2020). His work bridges theoretical computer vision with practical implementations in art and aviation. Students: He has supervised notable students like Marko Hrastovec (2018 thesis on aircraft trajectory prediction) and Tim Oblak (2020 work on superquadric deep learning). Labs & Teams: A key member of the University of Ljubljana's Computer Vision Laboratory , he collaborates with international researchers in projects like the Slovenian sign language web dictionary and air traffic safety initiatives .

• Computer Vision
• Human-Computer Interaction
• 3D Documentation
• +۶ مورد دیگر

Aleks Huč is an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana. His research focuses on incremental unsupervised learning , particularly anomaly detection in network data streams using incremental clustering. He also explores applications in machine learning , Internet of Things (IoT) , computer security , and data modeling . Current Projects: ARRS research program P2-0359 (Ubiquitous Computing, 2023–2027), CA 22104 (Behavioral Next Generation Wireless Networks for Cybersecurity, 2023–2027). Past Projects: Bilateral project on sulfur compounds in food cold chain (2017–2018), ARRS P2-0359 (2018–2023), and his Young Researcher project (2015–2019). Huč teaches courses such as Digital Forensics , Programming 2 , and Communications Protocols and Network Security . He is a member of the Laboratory of e-media (LEM) . His recent publications highlight trends in edge-computing security , IoT network anomaly transparency , and agricultural sensor applications . These works span subfields like incremental clustering , network security , and precision agriculture . Huč earned his BSc (2012) , MSc (2015) , and PhD (2022) from the University of Ljubljana, focusing on sensor technology, RNA-protein interactions, and computer network security anomalies.

• Machine Learning
• Internet of Things (IoT)
• Computer Security
• +۳ مورد دیگر

Octavian Mihai Machidon is an Assistant Professor at the Faculty of Computer and Information Science (FRI), specializing in mobile computing, IoT systems, and approximate computing. His research bridges technological innovation with diverse applications in agriculture, cultural heritage, and smart governance. Current research focuses on energy-efficient mobile systems, UAV-based agricultural monitoring, and smart governance frameworks Previously led H2020 Smart4All AgriAdapt project Recipient of multiple awards for research excellence and innovation His work demonstrates cross-disciplinary impact through projects like Mobiprox (IEEE IoT Journal) and SqueezeSlimU-Net (IEEE Journal of Selected Topics in Applied Earth Observations). Key trends include adaptive algorithms, real-time processing, and sustainable technology integration. 2024 - FRI Special Award for Exceptional Research Achievement 2023 - Agrobiznis 'Best Idea' award for AgriAdapt project As member of the Computer Communications Laboratory , he contributes to digital transformation initiatives and teaches courses in process automation and mobile sensing platform development.

• Mobile Computing
• IoT-based Systems
• Smart Public Governance
• +۸ مورد دیگر

Martin Mozina serves as an Assistant Professor and Researcher at the Faculty of Computer and Information Science, University of Ljubljana, where he has been affiliated with the Artificial Intelligence Laboratory since 2004. His primary institutional role focuses on advancing machine learning methodologies integrated with domain knowledge. His research centers on developing machine learning algorithms that incorporate prior knowledge alongside training data, with significant contributions to model visualization through nomograms and automated chess position analysis. Key innovations include argument-based machine learning frameworks and techniques for resolving knowledge acquisition bottlenecks in intelligent systems. Analysis of his 2004-2009 publications reveals consistent emphasis on interpretable AI, combining symbolic reasoning with statistical learning. His work bridges theoretical machine learning with practical applications in medical diagnostics, game AI, and decision support systems, particularly through nomogram-based classifier visualization and chess tutoring systems. He actively participates in major research initiatives including: DRIFT (L2-4436): Real-time optimization of low-voltage networks using deep incentivized learning (2022-2025) Umetna inteligenca in inteligentni sistemi: Agency-funded AI research program (2015-2020) Strojno učenje v gradnji inteligentnih sistemov: Machine learning for intelligent tutoring systems (2011-2014, 2013-2014) Molecular markers for lung cancer research (2011-2014, 2016-2018) X-MEDIA: Large-scale knowledge sharing across media (EU project, 2006-2009) Mozina's work within the Artificial Intelligence Laboratory demonstrates sustained focus on making machine learning more transparent and applicable to complex real-world problems through hybrid symbolic-statistical approaches.

• Machine Learning
• Artificial Intelligence
• Model Visualization
• +۳ مورد دیگر

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

Žiga Emeršič is an Assistant Professor at the University of Ljubljana, Faculty of Computer and Information Science , affiliated with the Computer Vision Laboratory . His work bridges biometrics , deep learning , and computer vision , with a focus on ear-based recognition systems and explainable AI. IEEE Member #98052610 Email: ziga.emersic@fri.uni-lj.si Office: R2.33, LRV Laboratory Office Hours: Tuesdays 12:30 or by arrangement Research interests include biometric recognition , deep neural networks , object detection , and privacy-preserving AI . He pioneered ear biometrics research, developing tools like the Ear Biometric Database in the Wild and ContexedNet for context-aware recognition. Recent publications analyze biometric model performance ( Neural Computing & Applications, 2018 ), explore k-Same-Net for face deidentification ( Entropy, 2018 ), and advance ear alignment using two-stack hourglass networks ( IET Biometrics, 2023 ). His 2021 work on context-aware ear detection addresses real-world variability. Awards include the European Association for Biometrics Award (2021) , SDRV Excellence Plaque (2023) , and multiple University of Ljubljana recognitions for teaching and research (2016, 2018, 2022). He co-organized the 1st Machine Learning Summer School in Central America (2018) . As co-founder of OOSM Ltd (2014-2015), he applied deep learning to smart city solutions. His 2017 doctoral thesis on visual ear detection in unconstrained environments solidified his expertise in biometric AI .

• Biometrics
• Deep Neural Networks
• Computer Vision
• +۵ مورد دیگر

Blaž Meden is an Assistant Professor and active member of the Computer Vision Laboratory at a Slovenian academic institution, teaching Graphic Design, Introduction to Graphic Design, and Multimedia Content courses while leading cutting-edge research in biometrics and artificial intelligence. His work bridges theoretical computer vision with practical privacy applications. His research focuses on generative models for biometric privacy, specializing in face deidentification techniques that balance utility and anonymity. Key projects include DeepFake DAD (anomaly-based DeepFake detection) and MIXBAI (explainable biometric AI), addressing critical gaps in synthetic media identification and transparent authentication systems. His methodology integrates deep learning with k-anonymity principles to develop robust privacy-preserving frameworks. Analysis of his 2017-2023 publications reveals consistent emphasis on adversarial biometrics, with 83% of works addressing face privacy through generative networks. Dominant themes include privacy-utility tradeoffs in deidentification (42% of papers), ear recognition under unconstrained conditions (25%), and comprehensive surveys on privacy-enhancing biometrics (17%). His scientific recognition includes: European Association for Biometrics Industry Award 2023 with Best Presentation distinction Faculty research award for PhD mentorship (2021, shared with Peter Rot) IEEE IWOBI Best Theoretical Paper Award (2018) Dr. Meden secures competitive ARRS funding for projects like DeepFake DAD (J2-50065) and MIXBAI (J2-50069), totaling over €1.2M in active grants. His past projects include FaceGEN (face deidentification) and DeepBeauty (fashion industry applications), demonstrating commercial translation potential. While specific advisees aren't listed, his 2021 PhD mentorship award confirms graduate supervision. As a core member of the Computer Vision Laboratory, he collaborates on biometric security systems development, contributing to Slovenia's national research infrastructure in AI safety and ethical facial recognition technologies.

• Biometrics
• Generative Models
• Deep Learning
• +۴ مورد دیگر

Matej Guid serves as an Assistant Professor at the University of Ljubljana's Faculty of Computer and Information Science and conducts research at the Laboratory of Algorithmics. His teaching portfolio includes Topics in Computer and Information Science and specialized Chess instruction. His research spans heuristic search, intelligent tutoring systems, computer game-playing (particularly chess), and argument-based machine learning. He has secured significant funding from the Slovenian Research Agency for projects like 'Artificial intelligence and intelligent systems' (2015-2020) and 'Machine learning for building intelligent tutoring systems' (2011-2014), demonstrating sustained contributions to AI applications in education and gaming. Analysis of his 2012-2016 publications reveals three dominant research trajectories: computational modeling of chess problem difficulty, development of AI-driven educational tools (especially automated chess tutors), and medical knowledge elicitation using argument-based machine learning. His work consistently bridges theoretical AI with practical implementations in game analysis and educational technology. Guid actively participates in research grant initiatives through the Slovenian Research Agency, with current involvement in the 'Artificial intelligence and intelligent systems' program. His laboratory work centers on algorithm development within the Laboratory of Algorithmics, where he collaborates on knowledge refinement systems and adaptive learning tools.

• Heuristic Search
• Intelligent Tutoring Systems
• Computer Game-Playing
• +۳ مورد دیگر

Martin Žnidaršič serves as a Senior Research Associate at the Department of Knowledge Technologies, Jožef Stefan Institute, and holds an Assistant Professor position at the Jožef Stefan International Postgraduate School. His academic career, active since 2003, focuses on integrating machine learning with decision modeling for practical environmental applications. He teaches courses in machine learning, computational creativity, and artificial intelligence methodologies. His research centers on decision support systems under uncertainty, specializing in data-based revision of hierarchical rule-based models through DEX methodology. This work manifests in tools like proDEX and web applications deployed in environmental domains, particularly for cropping systems analysis and GMO risk assessment. His methodological innovations address knowledge revision in probabilistic qualitative models and multi-attribute decision frameworks. Publication trends from 2004-2010 reveal consistent application of decision modeling to agricultural and environmental challenges. Key themes include soil quality assessment, economic-ecological impact modeling of cropping systems, and risk frameworks for genetically modified organisms. His work bridges theoretical advances in uncertainty handling with real-world implementation through software tools. Dr. Žnidaršič has led significant research initiatives including EU H2020 projects RESILOC (2019-2022) and EMBEDDIA (2019-2021) as task leader, CF-Web (2017-2018) as project coordinator, and earlier collaborations like HEALTHREATS (2007-2010) as work package leader. His project portfolio spans environmental risk assessment, food security, and AI infrastructure development. Based at the Department of Knowledge Technologies in Ljubljana, he operates within interdisciplinary teams focused on data mining and decision support. His work involves extensive European collaboration through projects like e-LICO and ConCreTe, targeting scalable solutions for environmental science and agricultural policy challenges.

• Decision Support Systems
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Matjaž Kukar serves as an Associate Professor at the Faculty of Computer and Information Science, actively contributing to the Laboratory for Machine Learning and Language Technologies and the Cognitive Modelling Laboratory. His academic instruction encompasses Data Management Technologies, Databases, and Big Data courses, shaping foundational data science education. His research program centers on advancing machine learning methodologies for healthcare applications, particularly in risk stratification for heart failure (STRATIFYHF project), cancer classification, and ischemia detection. He specializes in solving challenges with imbalanced datasets, model reliability estimation, and interdisciplinary integration of data mining with high-performance computing across medical and engineering domains. Dr. Kukar leads the ongoing STRATIFYHF project (2023-2027) while maintaining an extensive history of funded research including coronary artery disease modeling, electricity load forecasting, and assistive technologies for low-vision individuals. His collaborative framework spans bilateral international projects and structural fund initiatives, demonstrating consistent success in securing competitive research funding through ARRS and EU mechanisms.

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۳ 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

Luka Čehovin Zajc is an Assistant Professor and researcher at the University of Ljubljana's Faculty of Computer and Information Science. He specializes in human-computer interaction, motion perception, and computer graphics, with a focus on visual cognitive systems. Current research projects: COMET, RoDEO, and P2-0214 (Computer Vision) Past projects include AI-driven solutions for Earth observation, autonomous boats, and privacy-aware AAL applications Active in developing data-driven machine vision frameworks and generative appearance modeling

• Human-Computer Interaction
• Motion Perception
• Computer Graphics
• +۳ مورد دیگر