Applied Math background

Applied and Computational Mathematics

Master Applied & Computational Mathematics through modeling and computational methods, gaining practical skills to solve complex problems in science, engineering, and data-driven research.

Modeling and Applications

Mathematical Modeling and Applications

  • Mathematical Biology
  • Mathematical Models in Healthcare
  • Computational Molecular Medicine
  • Mathematical Game Theory
  • Foundational Methods in Applied Mathematics
  • Modeling, Simulation, and Monte Carlo
Computational Methods

Computational Methods and Scientific Computing

  • Computing for Applied Mathematics
  • Numerical Linear Algebra
  • Numerical Analysis
  • Numerical Methods for Partial Differential Equations
  • Scientific and High-Performance Computing
  • Advanced Differential Equations (Partial, Nonlinear, Numerical)
  • Stochastic Differential Equations: An Introduction With Applications
Optimization

Optimization and Algorithms

  • Optimization for Data Science
  • Introduction to Optimization I and II
  • Large-Scale Optimization for Data Science
  • Stochastic Optimization and Control
  • Combinatorial Optimization
  • Introduction to Convexity
  • Game Theory
Applied structures

Applied Structures and Theory

  • General Applied Mathematics
  • Applied Topology
  • Computational Complexity and Approximation
  • Combinatorial Analysis
  • Graph Theory
  • Cryptography / Cryptology and Coding
  • Introduction to Mathematical Cryptography
  • Optimal Transport
  • Equivariant Machine Learning
  • Probability and Stochastic Processes I & II
  • Theory of Probability