UC’s EMS program marries applied math, probability, and data science to solve complex engineering problems—from aerospace dynamics to bioinformatics.
Stochastic differential-equation model of stock-market microstructure noise
Finite-element solver benchmarking for viscoelastic material deformation
Bayesian hierarchical model estimating wildfire ignition risk factors
Gaussian-process emulator accelerating CFD jet-noise simulations
Policy brief demystifying statistical uncertainty in climate projections
Spectral-graph theory analysis of power-grid stability under renewables
Tensor-decomposition compression of deep neural-network weights
Optimal control algorithm for autonomous sailboat path planning
Interactive Jupyter notebook teaching bootstrapping and resampling
Network meta-analysis comparing COVID-19 vaccine efficacy trials
VR visualization of high-dimensional optimization landscapes
Compressed-sensing reconstruction for sparse seismic tomography data
Monte-Carlo radiative-transfer model of exoplanet atmospheres
Text-mining scientific abstracts to predict citation impact with Poisson GLMs
Data-assimilation workflow integrating satellite and buoy ocean data
Ethical guide to statistical disclosure control in public health datasets
Reinforcement-learning approach to adaptive experimental design
Eigenvalue study of vibrational modes in modular bridge segments
Crowdsourced platform validating probabilistic forecasts in sports analytics
Critical essay on algorithmic bias in predictive policing risk models
Model the unknown and quantify uncertainty with UC’s EMS powerhouse.
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