UC’s PhD in Engineering Mathematics & Statistics equips scholars to construct, analyze, and optimize mathematical models underpinning modern engineering and data-driven decision making.
Gaussian-process emulator accelerating multiphase CFD sensitivity studies
Finite-volume solver for wildfire propagation coupled to atmospheric models
Bayesian hierarchical model estimating bridge fatigue life from sensor data
Adjoint-based gradient optimization of supersonic nozzle contours
Sparse-regression discovery of governing equations in cardiac electrophysiology
Markov-chain Monte Carlo toolkit for real-time epidemic parameter inference
Mixed-integer program scheduling battery storage dispatch for price arbitrage
Random-matrix analysis of 5 G massive-MIMO channel capacity distributions
Kernel-density crash hotspot mapping incorporating weather covariates
Neural-ODE surrogate modeling lithium-ion cell thermal dynamics
Multilevel Monte Carlo valuation of renewable-energy derivatives
Topological data-analysis revealing coherent structures in jet turbulence
Hybrid genetic-algorithm + gradient method for satellite constellation design
Sequential experimental design for adaptive A/B testing in e-commerce
Game-theoretic airport slot allocation minimizing total delay costs
Uncertainty-quantified optimal transport for supply-chain logistics
Workshop teaching open-source math libraries to under-represented students
White paper on reproducible computational modeling standards
VR visualization of high-dimensional posterior landscapes
Conference on AI-accelerated scientific computing methods
Model, analyze, and optimize with UC’s mathematically rigorous PhD.
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