جستجوی استاد راهنمای دانشگاهی

Danijel Skočajمشاهده پروفایل

استاد

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

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.

Aleks Hučمشاهده پروفایل

استادیار

• Machine Learning
• Internet of Things (IoT)
• Computer Security
• +۳ 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
• Artificial Intelligence
• Model Visualization
• +۳ 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.

Ivan Bratkoمشاهده پروفایل

استاد

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +۵ 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.

Žiga Emeršičمشاهده پروفایل

استادیار

• Biometrics
• Deep Neural Networks
• Computer Vision
• +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 .

Blaž Medenمشاهده پروفایل

استادیار

• Biometrics
• Generative Models
• Deep Learning
• +۴ 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.

Sašo Živanovićمشاهده پروفایل

استادیار

• Semantics
• Quantification
• Phonology
• +۴ مورد دیگر

Sašo Živanović is an Assistant Professor at the Department of Comparative and General Linguistics, Faculty of Arts, University of Ljubljana. He holds a PhD in General Linguistics (2007) and a Diploma in Theoretical Mathematics (2002), and has been continuously employed at the university since 2007. His educational background reflects an interdisciplinary approach, combining mathematical rigor with linguistic theory. This foundation supports his research at the intersection of formal linguistics and computational methods. Živanović's research spans theoretical linguistics with particular emphasis on semantics (especially quantification), phonology, and syntax. His work demonstrates a consistent focus on the formal properties of language structure, with publications ranging from theoretical explorations of logical form to practical applications in linguistic documentation. His recent work shows increasing integration of computational approaches, particularly through his development of specialized LaTeX packages for linguistic research. Analysis of his publication record reveals a dual trajectory: theoretical contributions to linguistic theory (particularly in Slavic languages) and practical tools for linguistic documentation and analysis. His most recent publications demonstrate growing interest in computational linguistics and the development of specialized software tools for linguistic research. Živanović maintains an active presence in both academic and open-source communities, with significant contributions to the LaTeX ecosystem through packages like Memoize, Forest, and SignDraw that address specific needs in linguistic research and publication. As an educator, he teaches courses including Phonetics and Phonology, Linguistic Typology, Modern Linguistic Theories, and LaTeX for Linguists, demonstrating his commitment to equipping students with both theoretical knowledge and practical technical skills essential for contemporary linguistic research.

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.

Polona Vilarمشاهده پروفایل

استاد

• Information Behavior
• Information Literacy
• Reading Literacy
• +۳ مورد دیگر

Polona Vilar is a Professor at the Department of Library, Information Science and Book Studies, Faculty of Arts, University of Ljubljana, where she has worked since 1999. Her academic progression includes assistant professor (2008), associate professor (2014), and full professor (2019) positions. She teaches information behavior, information and reading literacy, and school/general library studies. Active in national and international research projects Visiting professor/researcher at City University London, University of Graz, and others Member of CoLIS and ISIC conference committees IFLA Section for Information Literacy committee until 2023 Senate member and Habilitation Commission member at Faculty of Law Her research focuses on information behavior, literacy, and library systems. Recent work examines research data management, academic reading formats, and archival literacy. She has over 140 publications including books and reports. Scientific awards include: Kalan Award (2003, 2019) from Slovenian Library Association Emerald/EFMD Publishing House Award (2008) for doctoral work ŠS FF Award (2013) for pedagogical excellence Vilar has mentored nearly 100 students and led projects like Kulturni in sistemski dejavniki bralne pismenosti (2015-2016). She actively promotes librarian continuing education and has brought international conferences (CoLIS 2019) to Slovenia.

Luka Čehovin Zajcمشاهده پروفایل

استادیار

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

Franc Jagerمشاهده پروفایل

استاد

• biomedical signal processing
• biomedical image processing
• digital signal processing
• +۴ مورد دیگر

Franc Jager is a Full Professor at the University of Ljubljana, Faculty of Medicine, where he leads the Laboratory for Biomedical Computer Systems and Imaging. His research focuses on biomedical signal and image processing, digital signal processing, biomedical computer systems, and human-computer interaction, with particular emphasis on electrohysterography (EHG) for pregnancy monitoring and preterm birth prediction. Professor Jager's research interests include: Biomedical signal processing and analysis Electrohysterogram (EHG) analysis for pregnancy monitoring Preterm birth prediction using uterine electrical activity Development of biomedical datasets and databases Biomedical computer systems and imaging His recent work has centered on developing and analyzing EHG datasets, including the Term-Preterm EHG DataBase (TPEHG DB), Term-Preterm ElectroHysteroGram DataSet with Tocogram (TPEHGT DS), and the Induced Cesarean EHG DataSet (ICEHG DS). These datasets have been instrumental in advancing research on predicting preterm birth and understanding uterine electrical activity during pregnancy. His publications reveal a strong focus on signal processing techniques applied to obstetric care, with particular attention to developing robust methods for analyzing uterine electrical activity across different pregnancy outcomes. Professor Jager is a Full Member of Sigma Xi, The Scientific Research Honor Society, recognizing his significant contributions to scientific research. He teaches courses in Biomedical Signal and Image Processing and Digital Signal Processing, mentoring the next generation of biomedical engineers and researchers. His laboratory pursues multiple research goals including understanding physiological phenomena, developing computer models of physiological relationships, monitoring physiological events, and creating standardized databases to evaluate recognition techniques in biomedical applications.

Borut Batageljمشاهده پروفایل

استادیار

• Pattern Recognition
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Borut Batagelj is Assistant Professor at the University of Ljubljana, Faculty of Computer and Information Science, and an active member of the Image-Based Biometry Group in the Computer Vision Laboratory. His expertise lies in pattern recognition, computer vision, and machine learning, with a strong focus on biometric forensics, face recognition, and deepfake detection. Education: Ph.D. in Computer Science, University of Ljubljana, Faculty of Computer and Information Science, 2007 M.Sc. thesis: Iskanje obrazov na osnovi barv s pomočjo statističnih metod razpoznavanja vzorcev , 2004 Research Interests revolve around forensic methods for detecting deepfakes and face-morphing attacks, biometrics from surveillance imagery, and interactive computer-vision systems for education and art. He employs anomaly-detection techniques, deep generative models, and real-time image analysis to advance these fields. His publication trend over the past decade shows a steady output spanning from theoretical pattern-recognition studies to practical forensic applications and multimedia art preservation. Notable themes include biometric security, educational technology, and augmented-reality visualization. Research Grants & Projects : Principal investigator: ARRS project J2-50065 “DeepFake DAD” (Deepfake Detection via Anomaly Detection), 2023–2026 Co-investigator: ARRS research programme P2-0214 “Computer Vision”, 2019–2024 Co-investigator: ARRS project J2-1734 “FaceGEN” (Face De-identification with Deep Generative Models), 2019–2022 Co-investigator: ARRS project J7-3158 “Sustainable digital preservation of the Slovenian new-media art”, 2021–2024 Earlier: ARRS projects on superquadric segmentation, interactive installations, and educational games. Laboratory & Team : He collaborates closely within the Computer Vision Laboratory , particularly the Image-Based Biometry Group, fostering interdisciplinary work between security applications and interactive media.

Peter Rotمشاهده پروفایل

پژوهشگر

• Computer Vision
• Biometrics
• Deep Learning
• +۶ مورد دیگر

Peter Rot is a researcher at the Faculty of Computer and Information Science , University of Ljubljana, specializing in Computer Vision and Biometrics . He is affiliated with the Computer Vision Laboratory (LRV) and has contributed to privacy-enhancing technologies in biometric systems. Education: Master of Computer Science and Informatics (University of Ljubljana, 2018) with thesis on "Deep Learning Methods for Biometric Recognition Based on Eye Information" Research Focus: His work addresses soft-biometric privacy , deepfake localization , and ocular biometrics , combining deep learning and generative models to protect facial attributes while maintaining recognition accuracy. Notable projects include FaceGEN (ARRS J2-1734) and DeepBeauty (ARRS J2-2501). Publications & Trends: Recent work explores privacy-preserving face analytics , interpretable biometric templates , and gender privacy through disentangled representations . Earlier studies focused on sclera segmentation and periocular recognition . Scientific Awards: European Association for Biometrics Biometric Research Award 2024 EAB Best Presentation Award 2024 University Prešernova Award 2018 Sclera Segmentation Competition Winner 2018 Sclera Segmentation and Eye Recognition Competition Winner 2017 Projects: Currently leads the MIXBAI project (ARRS J2-50069) on explainable biometric AI (2023-2026). Past projects include FaceGEN (2019-2022) for face de-identification and DeepBeauty (2020-2023) for generative models in fashion/beauty industries.

Boštjan Slivnikمشاهده پروفایل

استادیار

• Compilers
• Grid Technology
• Parallel Systems
• +۷ مورد دیگر

Boštjan Slivnik is an Assistant Professor specializing in compilers and grid technology , with a focus on parser algorithms and distributed systems. His research extends to domain-specific languages, big data optimization, and medical informatics applications. Program Committee member of ACM Symposium on Applied Computing (2007, 2008) Laboratory of Algorithmics member He teaches courses including Compilers , Compilers and Virtual Machines , and Programming 2 . His work involves projects like ARRS research programs on parallel systems and KATARINA's computational knowledge dissemination initiatives.

Denis Trčekمشاهده پروفایل

استاد

• Information Security
• Privacy
• Ubiquitous Computing
• +۵ مورد دیگر

Denis Trček serves as a Full Professor at the Faculty of Computer and Information Science, University of Ljubljana, and heads the Laboratory of e-media. His research spans critical domains in modern computing infrastructure with institutional impact. His primary research interests focus on: Information Security and Privacy frameworks Ubiquitous and Pervasive Computing systems Cyber Security for wireless networks Electronic Business process modeling Visual Information representation Current leadership includes the ARRS Ubiquitous Computing programme (P2-0359, 2023-2027) and the COST Action CA 22104 on Behavioral Next Generation Wireless Networks. Historical projects reveal sustained expertise in Trust Management systems, IoT implications, and European security initiatives like SALUS and CRYPTACUS. He teaches advanced courses including Information Security and Privacy, Concepts for Modeling Visual Information, and Electronic Business. Laboratory oversight and project coordination demonstrate integrated academic-industry collaboration in Slovenia's research ecosystem.