Organizers: Mohammad A. AlQudah, German Jordanian University, Amman 11180, Jordan
Banan Maayah, The University of Jordan, Amman, Jordan
Maalee Almheidat, The University of Jordan, Amman, Jordan
Ayat Almomani, Yarmouk University, Irbid, Jordan
Email: mohammad.qudah@gju.edu.jo, b.maayah@ju.edu.jo, m.almheidat@ju.edu.jo, ayat.momani@yu.edu.jo,
This mini-symposium aims to provide a dynamic platform for researchers in statistics and applied mathematics to present and discuss recent advances in theory and applications. The session will emphasize the integration of statistical modeling, computational techniques, and analytical methods in solving contemporary scientific and engineering problems. Participants will explore topics ranging from modern statistical inference and data analysis to optimization,
fractional calculus, and computational mathematics. The symposium encourages interdisciplinary contributions and fosters collaboration among young researchers, offering constructive feedback, networking opportunities, and exposure to emerging research directions.
By bridging theoretical developments with real-world applications, the session seeks to inspire innovation and support the professional growth of junior mathematicians and statisticians.
Symposium Tracks:
Track 1: Statistical Modeling, Data Analysis, and Intelligent Learning
This track focuses on modern statistical methodologies integrated with data-driven and intelligent learning techniques. It welcomes both theoretical and applied contributions addressing contemporary data challenges.
Topics include (but are not limited to):
- Statistical inference (frequentist and Bayesian analysis)
- Missing data techniques and imputation methods
- Regression models (linear, nonlinear, and generalized models)
- Multivariate and high-dimensional data analysis
- Machine learning and deep learning in statistical modeling
- Statistical learning theory and predictive analytics
- Time series analysis and forecasting
- Experimental design and survey methodology
- Data-driven applications in engineering, healthcare, economics, and social sciences
Track 2: Optimization, Computational Methods, and Simulation
This track emphasizes advanced computational techniques, optimization frameworks, and simulation-based approaches for solving complex problems in applied mathematics and statistics.
Topics include:
- Numerical optimization (convex, non-convex, global optimization)
- Metaheuristic and evolutionary algorithms
- Scientific computing and numerical linear algebra
- Modeling and simulation of complex systems
- High-performance and parallel computing
- Monte Carlo methods and stochastic simulation
- Data-driven and AI-based optimization techniques
- Inverse problems and computational modeling
- Applications in engineering, artificial intelligence, and operations research
Track 3: Fractional Calculus, Analytical Methods, and Kernel Techniques
This track highlights theoretical and applied advances in fractional calculus and analytical frameworks, with strong emphasis on computational and approximation techniques.
Topics include:
- Fractional differential and integral equations
- Numerical solutions of fractional systems and fractional integrodifferential equations
- Analytical and semi-analytical solution methods
- Special functions and generalized polynomials
- Stability and qualitative analysis of fractional systems
- Nonlinear dynamics and complex systems
- Applications in physics, biology, fluid dynamics, and control theory
- Connections with geometric function theory and q-calculus
Track 4: Stochastic Processes, Applied Probability, and Uncertainty Quantification
This track explores probabilistic modeling, stochastic systems, and uncertainty quantification in both theory and applications.
Topics include:
- Stochastic processes (Markov chains, diffusion processes, Lévy processes)
- Time series and stochastic forecasting models
- Financial mathematics and risk analysis
- Reliability theory and survival analysis
- Queueing systems and operations research
- Probabilistic machine learning and uncertainty quantification
- Random processes in engineering and applied sciences
- Statistical and stochastic modeling under uncertainty
- Applications in economics, insurance, and data science