As data structures become larger and more complex, methods of data analysis must evolve to harness the most accurate insights. Inspired by the challenges presented …
Combinatorial optimization problems are fundamental in many real-world applications, where the goal is to find the optimal or near-optimal solution to a problem subject to …
Autism is a developmental disability with variability in phenotypic features and genetic characteristics and affects 1% of children worldwide. Genetic and environmental factors are considered …
Inland waters (lakes, rivers, reservoirs, wetlands) are the largest natural source of the greenhouse gas methane, representing 40-50% of total emissions. Emission sources are characterized …
Hamilton-Jacobi partial differential equations (HJ PDEs) have deep connections to a wide range of scientific disciplines including optimal control, differential games, imaging sciences, and machine …
Advances in sequencing technologies in the last decade have enabled us to profile various genomic features at the single-cell resolution, such as gene expression and …
Erroneous radiotherapy prescriptions (Rx) can lead to injury or death of patients. A machine learning model developed in our previous work employs similarity learning to …
Adjusting for pre-specified baseline covariates in randomized trials can result in efficiency gains for the estimated effects. Even under misspecification of the adjustment model, the …
The goal of this study is to create a simulation-trained convolutional neural network (CNN) that can detect and characterize stiffer tissue embedded in a softer …
This dissertation presents a comprehensive, systematic investigation of methods and applications of scientific machine learning (SciML) in mechanics and their broader implications for interdisciplinary research. …
Structural integrity is essential for critical load bearing structures such as pipelines. Accurate characterization of hidden flaws, theoretical modeling of structural materials, and novel computational …
How the performance of black box machine learning algorithms (e.g., deep learning models) is affected by different factors can be hard to evaluate. Thus, in …
This dissertation is motivated by complex data architectures—typical in genetics research—that break the assumptions of classical frequentist analyses. Predictors are not necessarily independent nor fewer …
Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains …
Future collaborative robots must be capable of finding objects. As such a fundamental skill, we expect object search to eventually become an off-the-shelf capability for …
Biological brains are dynamic. Recent advances in electrophysiology and neuroimaging have helped uncover various mechanisms through which brains construct and utilize rich variations in neural …
This thesis presents the applications of phase field modeling in fracture analysis. In this method, a diffusive crack zone controlled by a scalar auxiliary variable …
The field of computer assisted radiology has recently seen an explosion of novel techniques that have been made possible due to advancements in computer algorithms. …
Aortic dissection is responsible for significant morbidity and mortality in children, young and older adults. One possible outcome for an artery undergoing dissection is that …