جست‌وجوی استاد و پژوهشگر

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.

Ajda Pretnar Žagarمشاهده پروفایل

پژوهشگر

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

Franc Solinaمشاهده پروفایل

استاد

• Computer Vision
• Human-Computer Interaction
• 3D Documentation
• +۶ 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 .

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.

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.

• Computer Vision
• Artificial Intelligence
• Image Processing
• +۳ مورد دیگر

Dr. Iyyakutti Iyappan Ganapathi serves as a Researcher and Laboratory Member at the Computer Vision Laboratory, focusing on advanced visual data analysis and computational imaging systems. His core research domains span: Computer Vision Artificial Intelligence Image Processing Pattern Recognition Deep Learning Machine Learning No publication trends or article classifications are documented in the source text. Scientific awards and honors remain unlisted. Advisory roles, grant funding, and laboratory team structures lack explicit mention in available materials.

Nejc Ilcمشاهده پروفایل

استادیار

• Machine Learning
• Cluster Analysis
• Bioinformatics
• +۲ 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.

Matej Kristanمشاهده پروفایل

استاد

• Computer Vision
• Visual Object Tracking
• Detection and Segmentation
• +۳ مورد دیگر

Matej Kristan is a Full Professor at the Faculty of Computer and Information Science , University of Ljubljana, where he serves as Vice Chair of the Department of Artificial Intelligence . He leads the Visual Object Tracking (VOT) Initiative , presides over the IAPR Slovenian Pattern Recognition Society , and acts as Associate Editor for IJCV . PhD from Faculty of Electrical Engineering, UL (2008) Co-organized 13+ workshops/conferences Research Interests focus on: Visual Object Tracking : Long-term re-detection, transparent object tracking, segmentation-based methods Autonomous Boats : Obstacle detection, sensor fusion, maritime navigation Physics-Informed Deep Learning : Sea level forecasting (HIDRA models), climate prediction Surface Defect Detection : Real-time deep architectures for cracks/dents Scientific Excellence : 26 Research Excellence Awards (Slovenian Research Agency) 10 Teaching Excellence Awards (UL/FRI) Best Paper Awards: ISPA2015, BMVC2022, Pattern Recognition Journal 2024 Student Mentorship : Supervised 8 PhD students including Alan Lukežič (summa cum laude 2021), Domen Tabernik , and Peter Uršič . His students have won 23 awards including multiple Prešeren Awards and Uroš Seljak distinctions. Laboratory Involvement : Key member of the Visual Cognitive Systems Laboratory , contributing to multimodal perception systems for maritime robotics, educational robotics platforms, and advanced deep learning architectures.

Luka Šajnمشاهده پروفایل

استادیار

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Luka Šajn is an Assistant Professor at the University of Ljubljana , affiliated with the Computer Vision Laboratory . His work bridges computer science , machine learning , and medical imaging , focusing on automated analysis of medical data. Academic Rank: Assistant Professor University: University of Ljubljana Laboratory: Computer Vision Laboratory Šajn's research centers on multi-resolution pattern parametrization and its applications in medical diagnostics. His projects include automated segmentation of whole-body bone scintigrams , detection of white spot lesions in dentistry, and 3D documentation of cultural heritage using computer vision. Šajn's publications (2005–2020) span texture classification , image segmentation , and medical AI . Key subfields include dental diagnostics , coronary disease analysis , mobile vision systems , and ultra-wideband trajectory modeling .

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 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.

Aleš Jakličمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Machine Learning
• +۳ مورد دیگر

Aleš Jaklič is an Assistant Professor affiliated with the Computer Vision Laboratory . His work spans computer vision, 3D reconstruction, and educational technologies, with a focus on practical applications in archaeology, meteorology, and STEM-C education. Research interests include: 3D modeling from point clouds Superquadric parameter prediction from depth images IoT-based educational tools for computer science Historical artifact digitization Image processing algorithms His publications since 2000 demonstrate expertise in geometric modeling, computer vision, and educational technology. Highlights include work on superquadric recovery (2000, 2003), archaeological modeling (2015), and IoT education frameworks (2020). Recent research (2021) explores neural network approaches to depth image analysis. Active in research programs funded by the Slovenian Research Agency (ARRS) since 2009, including the ongoing P2-0214 - Computer Vision program (2019-2024). He has also contributed to the ŠIPK 5 project on IoT education (2020).