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Octavian Mihai MachidonView profile

Assistant Professor

• Mobile Computing
• IoT-based Systems
• Smart Public Governance
• +8 more

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.

Igor KononenkoView profile

Professor

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +3 more

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.

Nejc IlcView profile

Assistant Professor

• Machine Learning
• Cluster Analysis
• Bioinformatics
• +2 more

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.

Davor SlugaView profile

Assistant Professor

• High-Performance Computing
• Approximate Computing
• Parallel and Distributed Systems
• +4 more

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).

• Computer Architecture
• Parallel Processing
• Computer Arithmetic
• +5 more

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.

Uroš LotričView profile

Associate Professor

• Soft Computing
• Distributed Processing
• High Performance Computing

Uroš Lotrič is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. His academic and professional work spans research in soft computing methods, distributed processing, and high-performance computing applications. Education: BSc in Physics (1994), MSc in Computer Science (1997), PhD in Computer Science (2000), all from the University of Ljubljana. Research Interests He focuses on soft computing techniques, distributed systems, and their applications in industrial and computational domains. His work integrates neural networks, wavelet transforms, and predictive modeling to solve complex problems in fields like rubber processing and time series analysis. Scientific Awards Best Assistant 2007 Best Professor 2009 Projects and Laboratory He is involved in national and European research programs such as P2-0241, EUMaster4HPC, and ARISA. Additionally, he is a member of the Adaptive Systems and Parallel Processing laboratory, contributing to advancements in adaptive algorithms and parallel computing.

Jurij MiheličView profile

Associate Professor

• Algorithms
• Discrete Mathematics
• Combinatorial Optimization
• +8 more

Jurij Mihelič is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, and a member of the Laboratory for Algorithms and Data Structures where he conducts research in theoretical and applied computer science. His research interests include: Algorithms and discrete mathematics Combinatorial optimization Computational complexity Operating systems and system software Virtual machines and execution environments Algorithm engineering and experimental algorithmics Graph theory and network analysis Facility location and pattern matching Mihelič teaches courses in Operating Systems and Algorithms and Data Structures I. His research bridges theoretical computer science with practical applications, with a particular focus on developing efficient algorithms and system software solutions. His work spans both theoretical foundations and practical implementations, demonstrating a strong commitment to connecting academic research with real-world problems. His publications demonstrate consistent contributions to algorithm design, graph theory, and combinatorial optimization, showing a progression from purely theoretical work toward more applied research in recent years. Key themes include facility location problems, subgraph isomorphism algorithms, and educational tools for system software concepts. Mihelič has been actively involved in the academic community, serving on organizing committees for significant conferences including EuroCG 2015, LADS3 2014, and ALGO 2012. He is currently involved in multiple research projects, most notably the ARRS research program 'Parallel and distributed systems' (2020-2026), along with several other projects focused on graph optimization, big data, and system software development.

Rok ČešnovarView profile

Lecturer

• Embedded Systems
• Signal Processing
• Computational Statistics
• +3 more

Lecturer Rok Češnovar is affiliated with the Laboratory for Adaptive Systems and Parallel Processing (LASPP). His work focuses on computational methods in adaptive systems and parallel processing. Teaches classes in Computer Systems Organisation, Input-Output Systems, and Embedded Systems Active in research projects related to computationally intensive statistical analysis, sensor networks, and RISC-V vector processors Research Focus Rok Češnovar specializes in embedded systems and signal processing , with emphasis on computationally intensive methods and approximate computing . His work bridges theoretical statistics with practical implementation in adaptive systems. Project History He has contributed to projects including: ARRS-funded research on computationally intensive statistical methods (2016-2019) Decomposing cognition in working memory studies (J3-9264, 2018-2021) High-performance RISC-V vector processor computing (BI-HR/23-24-009, 2023-2025)

Alina Luminita MachidonView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Distributed Computing
• +3 more

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

Uroš ČibejView profile

Assistant Professor

• distributed systems
• scheduling theory
• complexity theory
• +3 more

Uroš Čibej serves as an Assistant Professor at the Faculty of Computer Science and Informatics, University of Ljubljana, where he teaches core courses including Theoretical Foundations of Computer Science, Computability Theory, and Algorithms and Data Structures. As a member of the Algorithmics Laboratory, he contributes to both educational and research initiatives within the institution. His research centers on distributed systems, scheduling theory, and complexity theory, with specialized focus on approximate, probabilistic, and distributed algorithms. This work addresses fundamental challenges in computational efficiency and scalability, particularly relevant to modern distributed computing environments and big data applications. Dr. Čibej actively participates in major research projects such as ARRS Program P2-0095 "Parallel and Distributed Systems" (2020-2026), ARRS Project N2-0171 "Graph Theory and Combinatorial Scientific Computing" (2021-2023), and earlier initiatives including "Graph Optimization and Big Data" (2016-2019). His project portfolio demonstrates consistent engagement in theoretical algorithm development with practical applications in network analysis and visualization. Within the Algorithmics Laboratory, he collaborates on advancing theoretical computer science methodologies while mentoring students through coursework in foundational programming and computational theory.

Veljko PejovićView profile

Associate Professor

• Mobile deep learning
• Approximate computing
• Resource-efficient computing
• +6 more

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.