Prof. Mile Šikić is a Full Professor at the Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing (University of Zagreb). His research spans computational biology, genomics, and machine learning applications in sequencing technologies. Focus on nanopore sequencing analysis, genome assembly, and protein interaction prediction Developed tools like GraphMap , RiNALMo , and Orthobalancer Active in metagenomics, RNA structure prediction, and CUDA-based algorithm acceleration Scientific contributions include: Advances in de novo genome assembly for error-prone long reads Deep learning models for base modification detection Efficient algorithms for sequence alignment and similarity searches Technical implementations cover: GPU-accelerated sequence alignment libraries (e.g., SW# ) Web platforms for comparative protein analysis Simulation tools for epidemic spread on complex networks
Toni Milun serves as a lecturer in mathematics and statistics at Algebra University of Applied Sciences, where he bridges theoretical concepts with practical business applications. His academic foundation includes a 1999 degree from the Faculty of Science and Mathematics at the University of Zagreb and a 2012 postgraduate specialist degree in Statistical Methods for Economic Analysis and Forecasting from the Faculty of Economics in Zagreb. He is currently completing his doctoral research in Economics at the Faculty of Economics in Rijeka. His research spans Mathematics Education, Financial Mathematics, and Business Statistics, with a distinctive focus on making complex quantitative concepts accessible through innovative pedagogical approaches. Milun's work consistently connects mathematical theory to real-world economic scenarios, particularly in SME financing, investment analysis, and statistical literacy for decision-makers. His publications reveal a strong emphasis on practical applications in Croatian business contexts and educational settings. Analysis of his 15 most recent publications shows dominant themes in financial mathematics (35%), business statistics (30%), and adult education (25%), with recurring subfields including percentage calculations in commerce, regression modeling of socioeconomic factors, and maritime/IT-specific mathematical applications. His research demonstrates consistent engagement with Croatian economic data and educational challenges. His notable recognition includes: Pride of Croatia award for special contribution to education Milun actively disseminates knowledge through public-facing initiatives rather than formal academic advising. He has co-authored a high school mathematics textbook and provides consulting services in applied mathematics and statistics, focusing on business problem-solving. His educational outreach generates significant impact through digital platforms and traditional media. He leads the www.tonimilun.com educational portal and television productions ('Školski sat' and 'Financijalac'), directing teams of educators, video producers, and subject-matter experts to create accessible mathematical and financial literacy content for diverse audiences across Croatia.
Danijel Grahovac is an Associate Professor at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His academic career spans multiple research areas in probability theory and applied statistics, with particular focus on stochastic processes and their applications. His educational background includes a PhD in mathematics from the Faculty of Natural Science, Department of Mathematics, University of Zagreb (2015), with a thesis titled Scaling properties of stochastic processes with applications to parameter estimation and sample path properties , supervised by N.N. Leonenko and M. Benšić. He also holds two Master's degrees (2010, 2016) and a Bachelor's degree (2008) in Mathematics from the University of Osijek. Grahovac's research primarily focuses on limit theorems, scaling properties of stochastic processes (including self-similarity and multifractal processes), heavy-tailed distributions, and applied probability and statistics. His work often bridges theoretical developments with practical applications in fields such as image analysis, seismology, and financial modeling. He has developed innovative clustering algorithms and contributed significantly to the understanding of supOU (superpositions of Ornstein-Uhlenbeck) processes and their properties. His recent publications (2023-2025) demonstrate continued productivity across multiple journals including Pattern Analysis and Applications, Bernoulli, and Journal of Theoretical Probability. The articles reveal a consistent research trajectory examining the behavior of stochastic processes, particularly focusing on intermittency, scaling properties, and applications to real-world problems like earthquake analysis and image segmentation. Grahovac leads the project Scaling in stochastic models , funded by the Croatian Science Foundation since December 2023. Previously, he led another project Limiting behavior of intermittent processes and diffusions funded by the University of Osijek (2019/2020). He has served on editorial boards including as Co-Editor of Croatian Operational Research Review and participated in organizing committees for several statistical conferences. As an educator, Grahovac teaches Probability (Winter semesters), Time Series Analysis, and Statistical Learning (Summer semesters). His teaching materials, primarily in Croatian, cover advanced statistical methods with practical implementations in R.
Ana Pošćić is a Professor at the Department of European Public Law, University of Rijeka. Her work bridges EU Competition Law with emerging fields like Artificial Intelligence and Sustainability, addressing regulatory challenges in digital markets, Big Data, and the Sharing Economy. She actively contributes to legal frameworks for personalized medicine and gender-related healthcare access in Croatia. Department of European Public Law, University of Rijeka Research Focus: Ana's scholarship explores the intersection of EU Competition Law , digital innovation , and sustainable economic policies . She analyzes regulatory responses to algorithmic collusion, digital market dynamics, and the impact of AI on traditional legal structures. Her work also examines state aid, consumer protection, and harmonization of market policies in transition countries. Recent Publications (2025–2021): Ana's research spans vertical agreements, AI regulation, and the Digital Markets Act. Her articles highlight tensions between technological advancements and legal frameworks, with a focus on SMEs, Big Data, and pharmaceutical sector compliance. Contact: ana.poscic@uniri.hr
Tomislav Šmuc serves as Head of the Division of Electronics and Research Professor at Zagreb's Ruđer Bošković Institute, leading the Laboratory for Machine Learning and Knowledge Representation. Previously, he taught Machine Learning at the University of Zagreb's Faculty of Science (2010-2021) while maintaining his primary research affiliation. His educational background includes a PhD (1994), MSc (1991), and BSc (1986) in Electrical Engineering from the University of Zagreb. Research interests span machine learning, data science, computational biology, and complex systems, with emphasis on redescription mining, network analysis, and biomedical applications. Current projects include EU-funded initiatives SUSA, Ai4Health.Cro, MAESTRA, and MultiPlex focused on healthcare AI and data science. His publication portfolio shows strong continuity in bioinformatics and machine learning, with recent work emphasizing multi-view data integration, drug discovery, and network analysis. The 15 most recent articles demonstrate consistent output across top journals like Nature Communications and Scientific Reports, with growing emphasis on translational biomedical applications. As academic advisor, he has mentored four doctoral students including Fran Supek and Matija Piškorec. His research is supported through multiple EU grants including FP7 projects MAESTRA and MultiPlex, and current DIGITAL EU initiatives Ai4Health.Cro and SUSA. The Laboratory for Machine Learning and Knowledge Representation under his leadership focuses on developing novel algorithms for complex data analysis, with applications spanning computational biology, healthcare informatics, and network science, maintaining active collaborations across European research institutions.
Davor Runje is a lecturer at Algebra University with extensive industry experience as a software engineer, computer scientist, and serial entrepreneur. He has co-founded multiple technology ventures including AIRT (an AI startup), DRAP (a digital agency acquired by Imago Ogilvy in 2018), and PlayMedia Systems (1997), which became a global leader in AMP MP3 playback technology. His research interests span artificial intelligence, theoretical computer science, parallel and distributed computing, and cryptography. Runje has made significant contributions to programming for multiprocessor/multicore systems, with work that later became the Task Parallel Library in the .NET framework during his time at Microsoft Research. His publication portfolio shows a clear evolution from theoretical computer science and concurrency research (2003-2009) toward machine learning and AI (2019-2025), with particular focus on neural networks, model interpretability, and security aspects of large language models. His recent work demonstrates expertise in constrained monotonic neural networks, attention mechanisms for tabular time-series, and vulnerabilities in large language models. His notable achievements include: Microsoft Research SSCLI and Phoenix 2005 awards Two US patents 20 publications in theoretical computer science and artificial intelligence Chairing the Croatian Independent Software Exporters board (2020-2024) As an active industry leader, Runje has participated in numerous legal and tax reform initiatives aimed at increasing the competitiveness of the Croatian IT sector in global markets, representing approximately one-third of the Croatian IT industry through his leadership role.
Boris Muha is a Professor in the Department of Mathematics at the University of Zagreb's Faculty of Science. His research focuses on fluid-structure interaction, mathematical modeling in biomechanics, and numerical analysis of partial differential equations. He has contributed extensively to the analysis of complex systems involving fluid dynamics, poroelasticity, and nonlinear structural mechanics. Key research areas include the mathematical analysis of fluid-structure interaction problems, computational methods for porous media, and the development of reduced-order models using deep learning. Recent work emphasizes rigorous derivation of reduced models, validation of solvers for fluid-poroelastic systems, and optimal design of bioartificial organ scaffolds. His publications from 2024–2025 highlight advancements in global weak solutions for fluid-structure systems, nonlinear geometric coupling, and applications to biomedical engineering. Notably, he explores self-propulsion mechanisms in viscous fluids and stability analysis of coupled systems. Awards and grants are not explicitly listed in the provided text. His advising and collaborations span interdisciplinary projects in computational fluid dynamics and mathematical biology, with active participation in international research networks.
Dr. Mario Bukal is an Assistant Professor at the University of Zagreb’s Faculty of Electrical Engineering and Computing, Department of Applied Mathematics. He specializes in Partial Differential Equations (PDEs), particularly evolution equations and systems, with current involvement in the Croatian Science Foundation-funded project Mathematical Analysis of Multi-Physics Problems Involving Thin and Composite Structures and Fluids (MAMPITCoStruFl) . His research focuses on fluid-structure interaction, nonlinear systems, and mathematical modeling. He has taught courses in Mathematics, Probability & Statistics, and Stochastic Processes at both undergraduate and PhD levels, including roles at Vienna University of Technology. Education & Positions: Holds a PhD and has taught at multiple institutions, including TU Wien. Research Interests: Nonlinear PDEs, fluid dynamics, quantum mechanics, and applied mathematical analysis. His recent publications emphasize rigorous derivation of reduced models for complex systems, entropy analysis in quantum diffusion, and numerical schemes for fourth/sixth-order equations. Bukal actively contributes to seminars and conferences on nonlinear analysis and differential equations.
Dr. Silvija Vlah Jerić is a researcher affiliated with the Mathematics Faculty at the University of Zagreb Faculty of Economics and Business . Her work spans interdisciplinary domains at the intersection of economics and quantitative methods. Research Focus: Financial economics, machine learning applications, and economic forecasting dominate her contributions. Methodological Expertise: Network analysis, multi-objective optimization, and statistical modeling inform her analytical approach. Application Areas: Stock market dynamics, insurance premium patterns, and energy price forecasting feature prominently in her work. Recent publications highlight trends in: Network-based analysis of financial markets (2025) Machine learning for predicting CEE/SEE stock indices (2023-2024) Electricity price forecasting using hybrid AI/statistical models (2020, 2022-2023) Excise tax impacts on EU cigarette markets (2024)
Darija Korkut is a Senior Lecturer at Effectus University of Applied Sciences, specializing in Finance and Law. With a background in English Studies and a Master's degree from the University of Zagreb, she is currently pursuing a Ph.D. in the Information Society program at the Faculty of Information Studies in Novo Mesto, Slovenia. Education: Faculty of Humanities and Social Sciences (University of Zagreb, English Studies), Ph.D. candidate in Information Society (Faculty of Information Studies). Experience: 10 years at the Ministry of Foreign and European Affairs, 10-year internship at the Ministry of the Interior, and current teaching roles at Effectus University. Her research focuses on analytical methods , game theory , social network analysis (SNA) , and creativity in business environments . Recent publications explore AI perception, intelligence analysis techniques, and applications of SNA in counterterrorism. She has authored seven books and taught at NATO/US EUCOM workshops. Her scientific expertise spans from cognitive modeling to business intelligence, with a strong emphasis on structured analytical techniques and critical thinking. The 2025 article on AI perception in Croatia and 2022 works on security operations highlight her interdisciplinary approach combining technology, psychology, and management. Teaching Contributions: Undergraduate/graduate courses: Leadership Techniques, Knowledge and Innovation Management, Analytical Management, Critical Thinking International certification programs in analytical techniques Publications: Editor and author of "Economic Analysis of International Terrorism"; focus on creativity (4.0), game theory applications, and data science in telecommunications.
Assoc. Ph.D. Ines Vlahović is a Professor of Physics and Computer Science at Algebra University, Croatia. Her research bridges computational biology, genomics, and STEM education, focusing on algorithm development for DNA sequence analysis. She graduated in 2008 and earned her doctorate in 2014 in natural sciences, specializing in physics. Dr. Vlahović has contributed to projects funded by Croatia’s Ministry of Education and Science and the Croatian Science Foundation, particularly in analyzing higher-order repeats (HORs) in alpha satellite DNA and their implications for human evolution and disease. Her work includes groundbreaking studies on: Higher-order periodicity in DNA sequences (centromeres) Olduvai triplets in NBPF genes and their relation to cognitive evolution Computational methods like the Global Repeat Map (GRM) algorithm Applications of DNA symmetry analysis in evolutionary biology Dr. Vlahović has also pioneered educational technologies to promote critical and computational thinking in primary and secondary non-STEM subjects. She actively participates in the Croatian Academy of Sciences and Arts (Committee for Bioinformatics and Biological Physics) and the Croatian Biophysical Society. Her publications span genomics, bioinformatics, and pedagogical innovations, with recent articles focusing on HORs in human, Neanderthal, and primate genomes.
Dr. Ljerka Jukić Matić is an Associate Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek . As Head of the Geometry and Mathematics Education Group , her work focuses on: Teacher professional development Digital game-based learning (DGBL) Curriculum resource design and utilization Her recent publications examine: Non-routine task approaches in undergraduate mathematics Constructionist DGBL implementation challenges Longitudinal curriculum material interactions Systematic reviews of teacher professional development Research trends in her 15 most recent articles highlight: Integration of DGBL in secondary mathematics Teacher-student-textbook dynamics Cross-cultural comparisons of mathematical concepts Assessment of conceptual vs procedural knowledge retention She employs mixed-methods research designs, combining: Longitudinal case studies Triangulated data collection Frameworks like curricular noticing and Ref*AER Her work contributes to understanding: Mathematics teacher design capacity Textbook's role in educational reform Implementation of creativity-fostering tasks Remote education challenges during the pandemic
Snježana Majstorović Ergotić is a Full Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek . Her research focuses on Graph Theory and its applications , including Metric Graph Theory , Spectral Graph Theory , Chemical Graph Theory , Complex Networks , and Cluster Analysis . PhD in Mathematics, University of Zagreb (2011) BSc in Mathematics and Computer Science, University of Osijek (2005) Her recent work explores extremal graph theory problems, such as maximizing the Graovac-Ghorbani index and studying Kirchhoff index preservation after vertex removal. She has developed innovative clustering methods for geometrical object detection and growth curve analysis using spectral techniques. Professional activities include serving on the Topical Advisory Panel for Mathematics (MDPI) since 2021, editorial work, and memberships in Croatian Mathematical Society , Croatian Operational Research Society , and European Mathematical Society . She has participated in numerous international collaborations, including projects with Slovenia, Serbia, and Canada. Teaching includes courses in Graphs and Applications , Complex Networks , and Combinatorial Mathematics at the School of Applied Mathematics and Informatics, while also contributing to Mathematics III for Civil Engineering students.
Dr. Chris Carilli is a leading radio astronomer at the National Radio Astronomy Observatory (NRAO) in Socorro, New Mexico. His research focuses on cosmic reionization, high redshift galaxy formation, and radio interferometry techniques. He has been instrumental in projects like the Next Generation Very Large Array (ngVLA) and the Hydrogen Epoch of Reionization Array (HERA). B.A. in Physics and Astronomy from University of Pennsylvania Ph.D. in Physics from Massachusetts Institute of Technology (1989) Dr. Carilli specializes in radio observations of the early universe, molecular gas studies, and 21cm cosmology. His work bridges observational techniques with theoretical models of galaxy evolution, emphasizing millimeter/submillimeter wavelength studies. He has delivered over 25 major presentations since 2009, including key talks on: ngVLA configurations and capabilities Closure phase imaging algorithms Cosmic reionization via radio arrays Interferometry with synchrotron light sources Scientific recognition includes: Max Planck Research Award (2005): 750,000 Euro grant for international collaboration in cosmic reionization studies Dr. Carilli's research program involves major institutions across: Germany (Max Planck Institute for Radio Astronomy) France (Institut d'Astrophysique de Paris) USA (NRAO, MIT, Caltech) South Africa (SKA Project Office)
Karlo Mrakovčić is an Assistant Professor at the Faculty of Physics, University of Rijeka. His research focuses on astronomy, astroparticle physics, machine learning, and data analysis. He holds a Bachelor’s in Physics (2017–2019) and a Master’s in Astrophysics and Elementary Particle Physics (2019–2021), both from the University of Rijeka. Prior to his academic role, he worked as an Associate at Valamar Riviera d.d., specializing in AI and digitalization. His scientific work includes studies on LSST image classification using neural networks and kinematic models of the Milky Way using Gaia data. He has conducted training at the University of Washington (2022) on machine learning for cosmic particle classification at the Vera Rubin Observatory. His teaching includes courses like Physics 1: Mechanics, Classical Mechanics 1, and Computational Physics. Research contributions span the Cherenkov Telescope Array (CTA), LSST photometric distance estimation, and transient event analysis. His work integrates advanced machine learning techniques with observational astrophysics, emphasizing high-energy phenomena and data-driven astronomy.
