Uncertainty is prevalent in engineering design and data-driven problems and, more broadly,
in decision making. Due to inherent risk-averseness and ambiguity about assumptions, it is …

Uncertainty is prevalent in engineering design, statistical learning, and decision making
broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to …

This paper addresses the limitations of standard uncertainty models, eg, robust (norm-
bounded) and stochastic (one fixed distribution, eg, Gaussian), and proposes to model …

We compare sparse grid stochastic collocation and Gaussian process emulation as
surrogates for the parameter-to-observation map of a groundwater flow problem related to …

Inverse problems in physical or biological sciences often involve recovering an unknown
parameter that is random. The sought-after quantity is a probability distribution of the …

This study utilizes an ensemble of feedforward neural network models to analyze large-
volume and high-dimensional consumer touchpoints and their impact on purchase …

The celebrated Takens' embedding theorem provides a theoretical foundation for
reconstructing the full state of a dynamical system from partial observations. However, the …

The paper studies uncertaintly quantification for evolution equations with various time-
dependent parameters that evolve as stochastic processes. Instead of a sensitivity analysis …

We study evolution equations of drift-diffusion type when various parameters are random.
Motivated by applications in pedestrian dynamics, we focus on the case when the total mass …

In this thesis, we propose a unified framework for dealing with distributional uncertainty in
decision-making problems in the areas of stochastic optimization, machine learning, and …