Georgia Tech’s MS in Applied Mathematics propels theorists into high-performance problem-solvers. Core sequences in numerical PDEs, stochastic processes, and optimization feed into lab rotations on climate modeling, cryptographic lattices, and neural-ODE solvers. Students access the Partnership for an Advanced Computing Environment (PACE) supercluster to run billion-cell simulations, while industrial seminars from Sandia, Two Sigma, and CDC reveal how proofs power policy and profit.
GPU-accelerated solver for Navier–Stokes turbulence in urban canopies
Algebraic-topology persistent-homology detector for cybersecurity anomalies
Bayesian inverse problem for geothermal reservoir parameter estimation
Optimal transport algorithm for fairness in machine-learning datasets
Sparse-grid spectral method for high-dimensional option pricing
Graph-theoretic contact-tracing model with adaptive vaccination strategy
Finite-element model of bio-printed heart valve mechanics
Markov-decision-process algorithm for energy-aware cloud task scheduling
Proof-assistant verification of homomorphic-encryption security bounds
Stochastic differential-equation simulation of supply-chain disruptions
Multigrid preconditioner for ice-sheet flow dynamics
Game-theoretic analysis of carbon-credit auction design
Topology optimization of lattice materials for hypersonic leading edges
Integro-differential model of neuronal firing with fractional calculus
Neural-PDE surrogate for rapid weather-forecast ensemble generation
Transform theory into computational breakthroughs.
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