جستجوی استاد راهنمای دانشگاهی

Mario Bukalمشاهده پروفایل

استادیار

• Partial Differential Equations (PDEs)
• Fluid Dynamics
• Nonlinear Systems
• +۳ مورد دیگر

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.

Nenad Šuvakمشاهده پروفایل

دانشیار

• Stochastic Processes
• Diffusion Models
• Statistical Inference for Stochastic Processes
• +۴ مورد دیگر

Nenad Šuvak is an Associate Professor at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His research focuses on diffusion processes and statistical analysis of stochastic systems, with applications spanning finance, epidemiology, neuroscience, and agricultural modeling. Education : PhD in Mathematics (University of Zagreb, 2010), BSc in Mathematics and Computer Science (University of Osijek, 2004) Šuvak's work extends classical stochastic models to heavy-tailed and fractional frameworks. He has developed diffusion models for EEG data in cerebral malaria patients, created non-linear stochastic models for dopamine cycles, and refined epidemic SEIR models for SARS-CoV-2. His methodological contributions include correlated continuous-time random walks and parameter estimation techniques for non-stationary diffusions. His publications demonstrate expertise in stochastic processes with heavy-tailed distributions, fractional calculus applications, and statistical inference for complex systems. Notably, he has worked on financial risk assessment (CROBEX modeling), epidemiological modeling (SARS-CoV-2 spread), and biomedical applications (EEG analysis). Professional Activities : Organizer of mathematical conferences, member of program committees (Young Statisticians Meetings), and reviewer for multiple scientific journals Projects : PI for stochastic models projects, participant in EU COST actions, and international bilateral collaborations

Josip Arnerićمشاهده پروفایل

پژوهشگر

• Econometric methods and models
• Financial time-series and volatility
• Univariate and multivariate GARCH models
• +۳ مورد دیگر

Josip Arnerić, PhD, is a researcher at the Department of Statistics within the Faculty of Economics & Business at the University of Zagreb. His work focuses on econometric modeling, financial time-series analysis, and high-frequency data applications. Academic affiliation: Faculty of Economics & Business, University of Zagreb Research focus: Financial econometrics, volatility modeling, neural networks Email: jarneric@efzg.hr Research Interests Arnerić specializes in econometric methods for financial markets, with particular expertise in: Univariate and multivariate GARCH models Stochastic processes and volatility dynamics High-frequency data analysis Machine learning applications to financial forecasting Market risk modeling and portfolio optimization Publication Trends His work spans econometric modeling of stock market volatility (2023-2025), housing affordability analysis (2024), and neural network applications in inflation forecasting (2021). He has also explored: Price jump identification techniques Gold as safe-haven asset Interdependencies between traditional and cryptocurrency markets