This MS equips quantitative thinkers to translate abstract mathematics into high-impact solutions—from climate-system simulation to algorithmic trading. Core courses dive deep into partial-differential equations, numerical linear algebra, stochastic calculus, and scientific-computing on GPUs. Interdisciplinary research seminars pair mathematicians with life-scientists, economists, and materials engineers, fostering models that predict protein folding, optimize power-grid resilience, or price exotic derivatives.
Simulate glacier-flow dynamics with finite-element shallow-ice approximations
Develop stochastic differential-equation models for high-frequency FX volatility
Optimize vaccine-distribution networks using integer programming and GIS data
Implement parallel multigrid solvers for turbulence DNS on NVIDIA CUDA
Model information diffusion on adaptive social networks via mean-field theory
Apply optimal-transport metrics to fairness constraints in machine learning
Create reduced-order models of lithium-ion battery thermal runaway
Analyze nonlinear wave propagation in metamaterials using spectral methods
Design option-pricing algorithms under rough volatility with Riemann–Liouville paths
Study predator–prey eco-epidemiology with delay-differential systems
Develop a variational auto-encoder discovering latent manifolds in climate data
Formulate game-theoretic strategies for smart-grid demand response
Model infectious-disease hotspot emergence via agent-based lattice simulations
Compute topological data-analysis signatures of neural activity patterns
Derive sensitivity bounds for drag-reduction shape optimization in CFD
Harness mathematical power to solve complex, interdisciplinary problems.
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