Đani Pašić is a lecturer at Algebra University of Applied Sciences , specializing in computer science. He holds international certifications in Java programming and SQL Server database management, and his research focuses on gamification and machine learning applications in education and sports. Education: Specialist graduate degree in computer science (2018) from Algebra University of Applied Sciences. Research Interests: His work explores how gamification principles can enhance student motivation and outcomes in programming courses. Additionally, he investigates machine learning models for predicting student dropout risks and applies gamification techniques to recreational sports, particularly tennis. Professional Work: Concurrently, he serves as a Senior Specialist Software Development Engineer at Ericsson Nikola Tesla, contributing to R&D projects. His expertise bridges academic research and industry software engineering practices. Key Publications: 2023: Project-based exams in education 2022: Long-term effects of gamification in programming 2021: Leaderboards in object-oriented programming 2020: ML models for student dropout prediction 2018: Gamification in sports 2018: Gamification solutions in tennis
Mislav Spajić is a lecturer at the University of Algebra , where he teaches modules in database basics, data warehouses, and SQL as part of the internal education program 'Data Nursery of Knowledge'. He is employed at Hrvatska poštanska banka dd as a data architect and works as an external consultant in data science at Airspect doo, focusing on deep learning and computer vision. BSc in Applied Computing, majoring in Data Science (2022) from the University of Algebra His professional expertise spans data warehousing, business intelligence, cloud computing, machine learning, computer vision, remote sensing, and unmanned aircraft systems. He applies these skills in both corporate and educational contexts.
Dr. Mirko Talajić is a lecturer and Head of the Department at Algebra University, with expertise spanning game theory, IT systems management (including data science and analytical systems), and leadership/human resources management. He holds a doctoral candidate status at the Faculty of Information Studies in Novo Mesto, Slovenia, and has earned certifications as a trainer in emotional intelligence and leadership. Education: Graduated from the Faculty of Science and Mathematics (Department of Mathematics) in Zagreb, Master of Science in Game Theory applications at the Faculty of Economics, Zagreb. His research interests focus on strategic interactions, digital transformation, and AI-driven systems, with recent publications exploring machine learning in education, CNN-based defect detection in renewable energy systems, and game theory applications in workforce diversity and social media strategy. He also serves as CIO at the Agency for Commercial Activities (AKD) d.o.o., where he applies his technical and managerial expertise. Scientific awards: None explicitly mentioned. His academic collaborations and lectureships emphasize bridging theoretical research with practical applications in IT, business analytics, and leadership training.
Marko Velić, Ph.D., is a lecturer at Algebra University of Applied Sciences. With a doctoral degree in machine learning from the Faculty of Organization and Informatics in Varaždin (2014), he has held senior technical roles at global companies including Facebook, Photomath, and Google, where he currently serves as Software Engineering Manager. His career spans founding an IT development company (2007-2013), leading AI departments at Styria Group (2015-2019), and pioneering engineering teams in London and Zagreb. Research Interests : Machine learning applications in data science Software engineering innovations Business analytics and digital transformation Scientific Awards : Recognitions from Microsoft Awards from Nvidia
Assoc. Prof. Dr. Sc. Goran Đambić is an Associate Professor at Algebra University in Croatia, focusing on information sciences , computer science , and digital technologies applied to education and social/economic challenges. His work bridges software engineering , machine learning , and cybersecurity with pedagogical innovation in higher education. MSc in 2007, Faculty of Electrical Engineering and Computing, Zagreb His research emphasizes machine learning and automated assessment in education (via systems like AcpSQL), cybersecurity (honeypots), and IoT applications for environmental monitoring. He has published over 25 scientific papers and contributed to advancements in Kubernetes orchestration , ARM/RISC-V architectures , and LPWAN networks . Recent publications highlight trends in AI-driven education , smart public transport , and secure business systems . His work spans SQL query evaluation , containerization , and blockchain applications in communication systems. As a lecturer and researcher, he has held roles at Algebra University since 2007, advancing from lecturer to assistant professor and scientific associate . While no specific scientific awards are listed, his career reflects continuous engagement in digital transformation and pedagogical innovation .
Frane Čačić Kenjerić is an Associate Professor at the Department of Food Technology, College of Food Technology, University of Osijek. His research focuses on the intersection of food technology, analytical chemistry, and machine learning applications in food safety. Scientific Area: Biotechnical Sciences Scientific Field: Food Technology Branch: Engineering Research interests include stable isotope analysis for honey authentication, AI-driven food quality inspection, fungal growth monitoring via image analysis, and nanotechnology interventions in mycotoxin regulation. He utilizes chemometrics and advanced spectroscopic techniques to address food origin verification and safety challenges. Publications reveal a strong emphasis on food processing automation, by-product valorization in bakery products, and sensor-based quality control. His work spans both theoretical and applied domains, integrating engineering principles with food safety protocols. Dr. Čačić Kenjerić supervises PhD students Marija Nosić (defending 2025) and Esma Karahmet Farhat (defending 2025). He has contributed to projects like "Modern Drying Methods in Food-Process Engineering" and participated in international conferences such as Flour-Bread and Ružički Days.
Assistant Professor Juraj Benić is a control engineer at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek, Croatia . His interdisciplinary work bridges control theory, fluid power systems, IoT, and data-driven maintenance , with applications ranging from forestry vehicles to unmanned aerial systems. Education PhD in Control Theory and Mechanical Engineering, 2022 – University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture MSc in Control Theory and Mechanical Engineering, 2017 – University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture BSc in Control Theory and Mechanical Engineering, 2015 – University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture Research Interests Prof. Benić’s core research areas include: Energy-efficient direct-driven hydraulic (DDH) systems for mobile machinery Hybrid-electric powertrains for forestry skidders and multirotor UAVs IoT-enabled predictive maintenance using convolutional neural networks and vibration analysis Fuzzy-logic and ontology-based controllers for electro-hydraulic systems Human-robot interaction and context-aware robotics Computational linguistics and digital corpora of Croatian dialects Publication Trends Since 2018 he has produced more than 25 peer-reviewed works. The 2023-2024 journal articles focus on hybrid UAV propulsion and comparative energy efficiency of hydraulic systems , while earlier works delve into fuzzy control ontologies , predictive maintenance via IIoT and fuel-saving hybrid skidders . Interspersed linguistic contributions showcase his versatility in digital dialectology. Scientific Awards & Recognition While explicit awards are not listed, he has delivered invited lectures (University of Maribor, 2021) and participated in study visits (South Kazakhstan State University, March 2024), indicating growing international recognition. Supervision & Collaboration He collaborates closely with colleagues from the University of Zagreb and University of Osijek, co-authoring with researchers such as D. Pavković, Ž. Šitum, M. Cipek, and D. Brezak. Student supervision is implied through experimental rigs and project descriptions, although specific advisee names are not provided. Laboratory & Field Infrastructure Research is supported by fully instrumented hydraulic test benches, a retrofitted deep-drilling rig, IoT accelerometer networks, and field-measurement campaigns on commercial skidders equipped with telematics (WIGO-E) for long-term fuel and energy-data logging.
Domagoj Matijević is an Associate Professor at the School of Applied Mathematics and Informatics within Josip Juraj Strossmayer University of Osijek . His academic career spans computational geometry, optimization algorithms, and bioinformatics applications. PhD in Computer Science (Algorithms and Complexity), Max-Planck-Institute for Computer Science, Saarbrücken (2007) MS in Computer Science, Saarland University (2002) BS in Mathematics and Computer Science, University of Osijek (2001) Research interests focus on Machine Learning , Computational Geometry , and Bioinformatics , particularly through software tools like Fortuna for RNA splicing analysis and Trajan for comparing single-cell trajectories. His work bridges theoretical computer science with practical implementations in C++ , Python , and CUDA for high-dimensional data processing. 2023 Best Paper Award at MIPRO's Artificial Intelligence Systems track Key contributor to Neural Network-Based Pollen Prediction and Well-Separated Pair Decomposition implementations Developed Trajan for dynamic pseudotime warping and Fortuna for novel splicing event detection Currently teaches Algorithm Complexity , Computational Geometry , and Embedded Systems . Past projects include NVIDIA-funded GPU implementations and German-Croatian collaborations on kinetic spanners.
Dr. Kristian Sabo is a Full Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek, Croatia. His research focuses on Applied and Numerical Mathematics with applications in Agriculture , Medicine , and Seismology . He has developed innovative clustering algorithms like adaptive Mahalanobis fuzzy clustering and hybrid RANSAC-DBSCAN methods. PhD (2007) and MSc (2003) in Mathematics, University of Zagreb BSc (1999) in Mathematics and Computer Science, University of Osijek Research Highlights : Created Minimal Distance Index for optimal cluster validation in complex datasets Developed DIRECT algorithm adaptations for global optimization in multi-dimensional partitions Applied clustering methods to earthquake analysis and medical imaging Software Development : Co-developed LeArEst R package for noisy image analysis Created Mathematica modules for pandemic forecasting models
Zoran Tomljanović is an Associate Professor and Vice-Dean for Teaching and Students at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His research focuses on numerical linear algebra, damping optimization in mechanical systems, control theory, and matrix equations, with significant contributions to model reduction and parametric system optimization. His work includes novel approaches to $H_{\infty}$ norm analysis for multi-agent systems, dimension reduction techniques for damped vibrational systems, and parametric dominant pole algorithms for semi-active damping optimization. These methods have been applied to problems in mechanical engineering, synchronization, and high-dimensional data partitioning. Key projects led by Tomljanović include Accelerated solution of optimal damping problems (DAAD 2021–2022), Vibration Reduction in Mechanical Systems (Croatian Science Foundation 2020–2023), and Robustness optimization of damped mechanical systems (DAAD 2017–2018). He has also contributed to educational initiatives through publications like Metode optimizacije (2014), a textbook on optimization methods. Professional activities include co-organizing the 8th Croatian Mathematical Congress (2024), Winter School on Model Reduction (2024), and multiple international workshops on optimal control and model reduction. Teaching responsibilities include courses on linear algebra and control theory applications.
Alexander von Humboldt Professor and Chair for Dynamics, Control, Machine Learning and Numerics at Friedrich-Alexander-Universität Erlangen-Nürnberg. Holds dual PhD from University of Basque Country and Université Pierre et Marie Curie. Secondary affiliations at University of Deusto and Autonomous University of Madrid. Research integrates partial differential equations, control theory, and machine learning. Develops computational frameworks for system optimization and dynamic modeling. Current focus includes neural transport in normalizing flows and PDE-based machine learning architectures. Recipient of Alexander von Humboldt Professorship (2019), three European Research Council Advanced Grants, and W.T. Reid Prize (2022). Editor-in-Chief of Mathematical Control and Related Fields. Founded Basque Center for Applied Mathematics and established Computational Mathematics Chair at Deusto Foundation. Supervised 30+ PhD students with work cited in 300+ publications.
Stjepan Šebek is an Assistant Professor at the Department of Applied Mathematics, Faculty of Electrical Engineering and Computing, University of Zagreb. His research focuses on stochastic processes, probability theory, and combinatorial models, with notable contributions to random walks, convex hull analysis, and Markov chain theory. He is based in room D-153 and can be reached at stjepan.sebek@fer.unizg.hr. His work bridges theoretical mathematics with applications in statistical physics and combinatorial optimization. Research interests include the study of random sequential adsorption, convex hull properties of stochastic processes, and functional limit theorems for stable processes. His publications often explore the interplay between discrete and continuous models, with a focus on geometric and probabilistic questions. Collaborations with researchers like W. Cygan, N. Sandrić, and T. Došlić highlight his engagement in international scientific networks. Recent articles reflect trends in analyzing the asymptotic behavior of stochastic systems, such as convex hulls of Brownian motion and random walks with drift. His work on jammed configurations and combinatorial settlement planning demonstrates interdisciplinary impact, merging mathematics with spatial planning and ecology. Despite not listing awards, his prolific publication record in top-tier journals underscores his academic contributions.
Rebeka Čorić is a Lecturer at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. She holds a PhD in Computing from the Faculty of Electrical Engineering and Computing, University of Zagreb (2021), an MSc in Mathematics from the University of Osijek (2014), and a BSc in Mathematics from the same institution (2011). Her research focuses on fitness landscape analysis, genetic programming, scheduling optimization, and container relocation problems. She actively contributes to projects like the Hyper-Heuristic Design for Container Relocation (Croatian Science Foundation, 2023-2027) and has co-authored over 10 peer-reviewed publications in journals like *Expert Systems with Applications* and *Journal of Language Modelling*. Her teaching includes courses on Heuristic Algorithms, Web Programming, Data Structures, and Deep Learning for Natural Language Processing. Her research has addressed diverse topics, including energy-efficient container relocation rules, Croatian verb stem analysis via neural networks, and pollen concentration prediction using RNN-based models like PollenNet. She has also explored hyperheuristic approaches to scheduling problems and fitness landscape analysis in genetic programming. Her work bridges theoretical advancements with practical applications in logistics, linguistics, and environmental science. Professionally, she has presented at international conferences (e.g., GECCO, MIPRO) and contributed to service activities like workshops at the Festival of Science and teaching at summer schools. Her current projects emphasize automated heuristic design and optimization techniques across multiple disciplines.
Bastiaan Quast is a researcher at the International Telecommunications Union (ITU), an international organization under the United Nations. He holds a PhD in Development Economics from the Graduate Institute Geneva (IHEID) and a Master’s in Quantitative Economics and Finance from the University of St. Gallen. His primary research interests include artificial neural networks (R and Python), machine learning, data science, microeconomic estimators, quantum machine learning, and complexity modeling. Dr. Quast has contributed to expanding machine learning analyses into statistics and social sciences through his development of the 'rnn' framework in R. Before joining ITU, he worked at the United Nations Conference on Trade and Development (UNCTAD), the Internet Society, and The Netherlands Development Finance Company (FMO) in The Hague. His career reflects a blend of academic research and applied work in international organizations.
Dr. Jovan Njegić is a lecturer in postgraduate studies and Head of AI & Data Lab at Diplo. He holds a doctorate in Economics with a focus on data science and a master's in Finance. Doctorate: Economics (Data Science applications) Master's: Finance Research interests span data science, machine learning, and artificial intelligence, with dual emphasis on philosophical and implementational perspectives. His work applies AI to diplomatic fields, including cybersecurity speech generation tools. Academic engagement combines technical expertise in data science with broader applications in international affairs. Notably, he developed Diplo's Speech Generator using Distil language technologies to analyze AI applications in diplomacy.
