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.
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.
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.
Assistant Professor Davor Sluga is a member of the Laboratory for Adaptive Systems and Parallel Processing (LASPP). His research focuses on high-performance computing, approximate computing, and parallel/distributed systems, with applications in sensor networks, medical video processing, and RISC-V architecture. His work spans multiple projects including: Synergy of technological systems (ARRS P2-0241, 2020-2026) EUROCC2 National Competence Center (2023-2025) ARISA AI Skills Alliance (2022-2026) Context-aware on-device approximate computing (ARRS J2-3047, 2021-2024) He has previously contributed to projects involving medical video annotation (2014-2015), soft computing for dynamic systems (2010-2011), and hypercomputing in Parkinson’s research (2019).
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.
Iztok Lebar Bajec is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, where he has been actively engaged in research and teaching since completing his PhD in 2005. His academic journey began with a Computer Science Programme at the Technical School Centre in Nova Gorica (1994), followed by undergraduate, master's, and doctoral studies at the University of Ljubljana. His research spans multiple interdisciplinary fields including computer graphics, fuzzy logic applications, quantum-dot cellular automata, and artificial life modeling. Professor Lebar Bajec's work uniquely bridges computer science with biological modeling, particularly in simulating collective animal behavior. His PhD dissertation focused on fuzzy logic modeling of bird flocking behavior, which has evolved into a significant research trajectory examining organized group dynamics in both biological and artificial systems. His recent publications reveal a dual research focus: on one hand, the application of fuzzy logic to model collective behavior in biological systems (particularly bird flocking and predator-prey interactions), and on the other hand, pioneering work in quantum-dot cellular automata for ternary computing systems. This unusual combination of research areas demonstrates his innovative approach to computational modeling across vastly different scales - from biological collectives to nanoscale computing elements. 3rd place at the 10th student paper competition, ERK'2000, Portorose, Slovenia (2000) Best high school graduate of the Technical School Centre in Nova Gorica, Slovenia (1994) Runner-up at the 17th Young Innovator Competition, Nova Gorica, Slovenia (1993) Professor Lebar Bajec has advised multiple graduate students including Jure Demšar (PhD), Miha Moškon (BSc), Primož Pečar (MSc), and Tomaž Orač (BSc). His current research is supported by several active projects including P2-0359 - Ubiquitous computing (2023-2027), J7-4642 - Fundamental research for development of speech resources and technologies for Slovenian (2022-2025), and PoVeJMo - Adaptive Natural Language Processing with Large Language Models (2023-2026). He is a member of the Computer Structures and Systems Laboratory where he has been active since 2000.
Dr. Alina Luminita Machidon is an academic researcher and assistant affiliated with the Computer Communications Laboratory. She actively contributes to projects focused on digital transformation, approximate computing, and smart public governance. Current affiliation: Computer Communications Laboratory Her research interests span: Digital transformation for public governance Adaptable distributed AI systems Resource-efficient computing Context-aware on-device AI Energy-efficient UAV-based agriculture Recent projects include: P2-0426 (2022-2027): Digital Transformation for Smart Public Governance N2-0393 (2025-2027): Approximate Computing for Distributed AI J2-3047 (2021-2024): Context-aware On-Device Approximate Computing AgriAdapt (2023): Energy-efficient UAV agriculture Prior projects: N2-0136 (2020-2021): Resource Efficiency in Smartphones
Matej Vitek is an Assistant and researcher at the Faculty of Computer and Information Science, University of Ljubljana, where he is a core member of the Computer Vision Laboratory (CVL). His work centers on advancing biometric security through innovative computer vision techniques, with primary focus on lightweight sclera recognition systems. His academic journey includes: BSc in Computer Science and Informatics and Mathematics and Physics (2015), University of Ljubljana MSc in Computer Science and Informatics and Mathematics and Physics (2018), University of Ljubljana PhD in Computer and Information Science (2024), University of Ljubljana Vitek's research expertise spans computer vision, biometrics, and deep learning, with specialized focus on sclera recognition. His methodology emphasizes developing computationally efficient models suitable for mobile deployment while addressing critical challenges like model bias and segmentation accuracy. Past explorations include quantum computing circuits and game development, demonstrating interdisciplinary versatility. Current work integrates anomaly detection for deepfake identification and explainable AI frameworks for biometric systems. Publication trends reveal consistent advancement in sclera biometrics through large-scale collaborative efforts, including benchmarking competitions and novel dataset creation. His work demonstrates progression from foundational segmentation studies toward optimized lightweight architectures and bias mitigation strategies, reflecting the field's evolution toward practical, ethical deployment. Vitek actively contributes to two major ARRS-funded projects: J2-50065 'DeepFake DAD' (2023-2026) developing anomaly detection methods for deepfake identification, and J2-50069 'MIXBAI' (2023-2026) creating interpretable mechanisms for explainable biometric AI. Previously, he participated in the P2-0214 Computer Vision research program (2019-2024) and consulting initiatives. As a CVL laboratory member, he collaborates within a specialized biometrics team led by Prof. Peter Peer (supervisor) and Prof. Vitomir Štruc (co-supervisor), working alongside researchers Peter Rot, Žiga Emeršič, and Blaž Meden. His technical environment combines academic research with practical implementation challenges in resource-constrained settings.
Slavko Žitnik is an Associate Professor and Vice-dean at the Faculty of Computer and Information Science, University of Ljubljana, where he is a member of the Laboratory for Data Technologies. His academic career spans multiple research projects and international collaborations focusing on data technologies and natural language processing. His primary research interests include information retrieval, information extraction, natural language processing, entity extraction, relationship extraction, coreference resolution, data merging, redundancy elimination, and ontologies. Dr. Žitnik's work often bridges theoretical computer science with practical applications in various domains including education, healthcare, and smart city ecosystems. Dr. Žitnik has led and participated in numerous significant research projects including P2-0359 on Ubiquitous Computing (2023-2027), PoVeJMo on Adaptive Natural Language Processing with Large Language Models (2023-2026), and the GOBLIN COST Action for building global networks of large-scale knowledge graphs. His recent work demonstrates a strong focus on adapting natural language processing techniques with large language models and creating practical applications of these technologies. His scientific contributions span multiple domains including: Natural Language Processing and Information Extraction techniques Knowledge graph construction and integration Applications in education, healthcare, and smart city ecosystems Development of practical tools and systems for data processing Dr. Žitnik has established international collaborations with institutions including Harvard University's Department of Biomedical Informatics (where he conducted a research visit from July to October 2022), the University of South Florida, and various European partners through COST Actions and other collaborative frameworks.
Veljko Pejović is an Associate Professor at the Faculty of Computer and Information Science (FRI), University of Ljubljana, Slovenia, where he also serves as Head of the Computer Communications Laboratory. His research focuses on mobile computing with special emphasis on resource efficiency in mobile and IoT environments. His educational background includes a PhD in Computer Science from the University of California, Santa Barbara (2012) and a dipl. ing (BS) from the University of Belgrade, Serbia (2006). Pejović's research interests center around mobile deep learning, approximate computing, and resource-efficient computing. His work explores how computation accuracy can be dynamically adapted based on contextual factors to optimize resource usage without significantly compromising user experience. He has made significant contributions to mobile sensing, machine learning on resource-constrained devices, and security in IoT environments. His recent publications reveal a strong trend toward making AI more efficient and accessible on mobile and edge devices, with particular focus on approximate computing techniques, federated learning frameworks, and context-aware adaptation of computational resources. His research spans applications from precision agriculture using UAVs to behavioral authentication in IoT environments and mental health inference from mobile sensor data. Scientific Awards: 10-Year Impact Runner Up Award at ACM UbiComp for InterruptMe work Outstanding research achievement award for 2024 by the Faculty of Computer and Information Science Best Paper Nominee (top 4%) at UbiComp'14 for InterruptMe Pejović actively mentors PhD and master's students, with numerous theses resulting in workshop, conference, and journal publications. He serves as Associate Editor for ACM IMWUT and ACM JCSS, and has held organizational roles in major conferences including ACM UbiComp 2025. His research is supported by multiple significant projects including approXimation for adaptable diStributed artificial intelligence (ARIS), CODA, AgriAdapt, and CARMA. His laboratory, the Computer Communications Laboratory at FRI, focuses on developing practical systems and frameworks for resource-efficient mobile computing, with several open-source tools and datasets publicly available for the research community.
