Aleš Smrdel is an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana. He is affiliated with the Laboratory for Biomedical Computer Systems and Imaging. Research Interests: Designing and implementing user interfaces Usability testing and human-computer interaction Biomedical signal analysis, particularly ECG and ischemia detection Selected Publications: Focus on algorithm development for cardiovascular diagnostics, including ST segment analysis and open-source tools for ECG evaluation. Laboratory: Active member of the Laboratory for Biomedical Computer Systems and Imaging.
Zoran Bosnić is a Full Professor at the Artificial Intelligence Department, Faculty of Computer and Information Science, University of Ljubljana. He specializes in integrating advanced statistical methods with practical applications including data stream mining, recommender systems, user behavior profiling, e-learning systems, and computer communications. B.Sc. (2001), M.Sc. (2003), and Ph.D. (2007) from Faculty of Computer and Information Science, University of Ljubljana, Slovenia His research focuses on computationally intensive methods for statistical analysis, inductive learning from partially labeled datasets, and AI applications in medical domains. He has contributed to projects involving quantum advantage in reservoir computing and cross-lingual embeddings for European news media. 2012 Certificate of Honor for young university teachers 2019 Gold Medal of the University of Ljubljana He is a member of the Laboratory for Machine Learning and Language Technologies and actively participates in both national (ARRS) and international projects (European, bilateral collaborations).
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.
Prof. Igor Kononenko serves as a Professor at the Faculty of Computer and Information Science, University of Ljubljana, where he heads the Laboratory for Cognitive Modeling and teaches core courses including Algorithms and Data Structures 1, Artificial Intelligence, Intelligent Systems, and Machine Learning. Education: Ph.D. in Computer Science, University of Ljubljana (1990) Research Focus: His work centers on Artificial Intelligence , Machine Learning , and Cognitive Modeling , with recent emphasis on explainable AI and prediction reliability . He has pioneered techniques in feature contribution explanation, graph-based data mining, and archetypal analysis for complex datasets, resulting in approximately 210 publications and 10 textbooks . Publication Trends: Analysis of his 15 most recent articles reveals a decisive shift toward interpretable machine learning—particularly reliability estimation in data streams (2012-2014), graph mining for oceanographic/spatial data (2013-2019), and medical applications of explanation methods (2011-2018). His 2013-2016 work on archetypal analysis for multi-document summarization remains highly influential in NLP. Research Leadership: As principal investigator, he secured major funding including: Two ARRS programmes on Artificial Intelligence (2009-2014, 2015-2020) EU's AGROIT project for farming efficiency (2014-2016) Bilateral projects on imbalanced data learning (2010-2011), bioinformatics for cancer classification (2014-2015), and disease dataset analysis (2020-2021) Laboratory: The Laboratory for Cognitive Modeling under his direction drives innovation in AI theory and applications, with recent work spanning basketball analytics, coronary artery disease diagnostics, and hemodynamic simulation modeling.
Jožef Stefan International Postgraduate SchoolSlovenia
Sašo Džeroski is a Professor at the Department of Knowledge Technologies, Jožef Stefan International Postgraduate School, and a Scientific Councillor at the Jožef Stefan Institute in Slovenia. He leads a research group of ~17 members, focusing on constraint-based data mining, structured output prediction, and automated modeling of dynamic systems, with applications in systems biology, ecology, and environmental sciences. His research interests span data mining, machine learning, computational scientific discovery, and their applications in life sciences and medicine. He has coordinated major projects like MAESTRA (FP7) and LANDMARK (H2020), and contributed to EU initiatives in systems biology ( E.E.T. Pipeline , PHAGOSYS ). He developed methodologies for predictive clustering trees and hierarchical multi-label classification, with software tools ProBMoT and CLUS . The 15 most recent articles highlight his work on ensemble learning for multi-output systems, structured output prediction in medical imaging, ecological modeling using machine learning, and ontologies for data mining. These publications reflect interdisciplinary trends combining computational methods with applications in fungal biology, endocytosis, and agricultural drainage. Scientific Awards: Foreign Member, Macedonian Academy of Sciences and Arts Advising includes mentoring PhD students (e.g., Nikola Simidjievski, Jovan Tanevski) and MSc students. Grants and projects emphasize EU funding (H2020, FP7) and national initiatives in knowledge technologies. He has served on editorial boards of journals like Machine Learning and Ecological Informatics , and organized key conferences (e.g., ECML PKDD-2015 ).
Sergio Cabello is a full professor at the Faculty of Mathematics and Physics of the University of Ljubljana, Slovenia, and a researcher at the Institute of Mathematics, Physics and Mechanics. His research focuses on discrete algorithms and computational geometry, with contributions to graph theory, geometric optimization, and algorithm design. He has held editorial roles in journals such as the Journal of Combinatorial Mathematics and Combinatorial Computing, and has participated in numerous conferences as a PC member or invited speaker. His work spans topics like shortest paths in geometric graphs, convex hulls, and algorithmic complexity. Notable recent activities include organizing the Indo-Spanish School of Algorithms and Combinatorics (2025) and serving on the editorial boards of Discrete Mathematics & Theoretical Computer Science and the Journal of Computational Geometry. He has also received best paper awards at SODA 2017 and AGILE 2009 for his contributions to theoretical computer science. Editorial Roles: Journal of Combinatorial Mathematics, Discrete Mathematics & Theoretical Computer Science, Indian Journal of Discrete Mathematics Professional Activities: PC member for EuroCG 2025, WADS steering committee member, invited speaker at GROW 2022 and Discrete Mathematics Days 2024 His research interests include geometric algorithms, graph connectivity, and combinatorial optimization, with a focus on theoretical foundations and algorithmic applications in discrete structures.
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.
Narvika Bovcon is a Full Professor at the Faculty of Computer and Information Science, specializing in video and new media art. Since 2016, she has served as editor-in-chief of the journal Art Words and has authored numerous new media art projects and exhibitions. Her academic activities include teaching courses such as Introduction to Graphics Design, Graphic Design, and Multimedia Content. Research Focus: Bovcon's work explores interdisciplinary intersections of technology and art, with emphasis on: Digital animation and mixed reality applications User interface design for cultural heritage Augmented reality in fine arts and literature Digital preservation methodologies Computational approaches to visual communications Publication Trends: Her recent scholarly output (2013-2021) demonstrates strong focus on digital heritage preservation, augmented reality implementations in cultural contexts, computational analysis of art, and educational applications of interactive media. Works frequently employ case studies and technical evaluations within Slovenian cultural frameworks. Research Projects: P2-0214 Computer Vision (ARRS, 2019-2024) - Core research programme J7-3158 Sustainable digital preservation of Slovenian new-media art (ARRS, 2021-2024) ŠIPK 2 Cultural Heritage documentation using new technologies (Structural Funds, 2018) PKP 1 Interactive computer installation integration (Structural Funds, 2014) Laboratory Affiliation: Active member of the Computer Vision Laboratory at the Faculty of Computer and Information Science, contributing to technical-artistic research initiatives.
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 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.
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.
Marko Toplak is a Researcher at the Faculty of Computer and Information Science, University of Ljubljana, serving as teaching assistant for Programming 1, Business Intelligence, and Introduction to Bioinformatics courses. His research specializes in machine learning with emphasis on background knowledge integration for predictive modeling. He collaborates with Baylor College of Medicine analyzing Dictyostelium discoideum gene expression data and develops the Orange data mining toolkit with its Bioinformatics add-on using Python. Currently leading ARRS projects J3-4513 (Role and applicability of circular RNA in liver cancer, 2022-2025) and L2-60154 (Explainable Foundation Models for Human Gene Expression, 2025-2027), he maintains active membership in the Bioinformatics Laboratory. His work bridges computational methods with biological applications across genomics and systems biology domains.
Rok Žitko is an Assistant Professor in the Department of Computer Technologies . His research focuses broadly on computational methods and their applications in modern technology. Academic Rank: Assistant Professor Department: Computer Technologies
Patricio Bulić is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. His primary research focuses on computer architecture, embedded systems, and approximate computing for energy-efficient hardware design. Research Areas: Computer Architecture, Parallel Processing, Embedded Systems, Approximate Computing Affiliation: Faculty of Computer and Information Science (University of Ljubljana) Laboratory: Laboratory for Adaptive Systems and Parallel Processing His recent publications explore logarithmic arithmetic and approximate computing techniques to optimize hardware performance while reducing power consumption. These methods have been applied in sensor networks, digital signal processing, and neural network implementations. Scientific Awards: Professor of the Year 2014 Professor of the Year 2020 Professor of the Year 2021 Professor of the Year 2022 He has led numerous research projects including the ARRS programme on Ubiquitous Computing and COST Action IC1303 for enhanced living environments. His teaching responsibilities include courses in Computer Systems Organization, Parallel and Distributed Systems, and Embedded Systems.
Dr. Žiga Emeršič is an Assistant Professor at the University of Ljubljana , Faculty of Computer and Information Science, and a member of the Computer Vision Laboratory (LRV). His research focuses on biometrics , deep neural networks , and computer vision with a specialization in ear-based recognition systems. IEEE Member (#98052610) Recipient of the European Biometrics Association Award (2021) and SDRV Excellence Plaque (2023) Co-organizer of international challenges in ear recognition and machine learning workshops His work addresses unconstrained ear detection , model compression for edge devices , and privacy-preserving biometric systems . He has contributed to AI education through EU projects like AIM@VET and developed curricula for computer vision and biometrics. Highlights: Published in top journals ( Neural Computing & Applications , IET Biometrics , Entropy ) Authored chapters in Springer publications on deep ear recognition and ocular biometrics Active in international conferences (IEEE, IAPR) with over 60 publications He has received special recognition for both research (2018) and teaching excellence (2016, 2019), and his work has been featured in media outlets across Slovenia.
