Department of Computational Mathematics
Advancing mathematical sciences through computational methods, modeling, and analysis to solve complex problems in technology and science.
About the Department
The Department of Computational Mathematics bridges pure mathematics with practical applications in technology and science. We focus on developing mathematical models and computational methods to solve complex real-world problems.
Our programs combine rigorous mathematical theory with modern computational tools, preparing students for careers in research, finance, technology, and data science.
Study Programs
Application of mathematical methods to solve real-world problems in science and engineering.
Computational methods for solving mathematical problems using computer algorithms.
Optimization techniques and decision-making tools for complex systems.
Mathematical models for financial markets, risk analysis, and quantitative finance.
Application Areas
Mathematical foundations for secure communication systems
Risk assessment and portfolio optimization in financial markets
Statistical methods for big data analysis and machine learning
Numerical simulations for physics, engineering, and biology
Research Focus
- • Advanced numerical methods
- • Optimization theory
- • Stochastic processes
- • Differential equations
- • Graph theory applications
- • Financial risk modeling
- • Cryptographic protocols
- • Machine learning algorithms
- • Scientific simulations
- • Data mining techniques
Computational Resources
Access to powerful computing clusters for complex mathematical simulations and large-scale numerical computations.
- • Multi-core processing systems
- • Parallel computing frameworks
- • Mathematical software suites
- • Cloud computing resources
Industry-standard mathematical software and programming environments for research and education.
- • MATLAB and Mathematica
- • Python scientific libraries
- • R for statistical analysis
- • Specialized mathematical packages