Georgia Tech Math PhD students prove theorems in geometric group theory one day and deploy GPU-accelerated Navier–Stokes solvers the next. Graduates land at NSF-funded AI institutes, hedge-fund quant desks, and national labs.
Homological stability in configuration spaces of graphs
Optimal transport metrics for bias in ML embeddings
Spectral methods for stochastic Navier–Stokes on GPUs
Random-matrix universality in massive MIMO communications
Proof-assistant formalization of prime gap bounds
Sparse FFT algorithms for seismic imaging
Game-theoretic study of carbon-credit auctions
Ergodic theory of billiards in irrational polygons
Neural-PDE surrogates with convergence proofs
Integer programming relaxations for graph coloring
Fractal dimension of urban land-use sprawl
KAM-theory extension to non-Hamiltonian bio oscillators
Hyperbolic geometry of social network growth
Bayesian inverse problems in geothermal reservoirs
Coupled bulk-surface PDEs for tumor growth
Prove ideas that become tomorrow’s transformative algorithms.
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