In recent years, the understanding and identification of quantum materials supporting non-trivial band topology has progressed rapidly. This progress has been motivated in part by the potential application of topological quantum materials in quantum computers, sensors, and other next-generation devices. Despite this progress, the computation of bulk topological invariants in...
This dissertation focuses on quantifying protein folding stability determinants and presenting initial experiments that can guide the development of a novel assay that identifies cell-penetrating miniproteins. First, despite over a century of scholarship on protein folding stability, applying this knowledge to design proteins computationally remains limited. Usually, protein designers generate...
This project explores how the variation in language experiences and attitudes that Mexican American Spanish heritage speaker bilinguals in the United States have affects their speech perception in both their languages. Heritage language bilinguals speak as a first language a minority language that they have cultural ties to (e.g., Spanish...
This dissertation investigates how scope relations are constructed and evaluated during real-time human sentence processing. Theoretical approaches to processing scope relations exist in a multi-dimensional space where trade-offs are made around how quickly scope relations can be computed, how many mistakes are made in computing scope relations, and how many...
The field of distributed optimization algorithms is wide and growing, and new techniques for categorizing and analyzing existing algorithms are continually being developed. In this Thesis, we leverage control theoretic analysis and block diagram interpretations for existing algorithms to synthesize entirely new families of algorithms. These new algorithm families have...
Transitioning energy systems from a reliance on fossil fuels to low carbon energy sources is an essential solution for climate change mitigation. However, the industrial sector, which is directly responsible for more than a quarter of global carbon dioxide (CO2) emissions, continues to use fossil fuels for energy and feedstocks....
Necessity is the mother of innovation. In the wake of the pandemic, with no flow of samples and limited fabrication techniques available, necessity demanded a new material platform and adaptable methods to make complex oxide samples worth measuring. The material platform was KTaO3 , the younger successor to the mainstay...
This manuscript describes and contextualizes the research I performed as a PhD student in Northwestern University. The first three chapters, on Markov chains, stochastic thermodynamics, and large deviation theory, describe three interrelated topics that serve as the background for subsequent research detailed in the next three chapters, on understanding the...
This dissertation aims to develop innovative analytical methods that integrate engineering, marketing, and social science disciplines to incorporate heterogeneous consumer preferences into product design using network-based customer preference modeling. Both companies and designers frequently face difficulties in understanding and addressing customer preferences, which can result in product failure and loss...
Machine learning and deep learning have been proven successful across various scientific fields, such as computer vision, natural language processing, and recommendation systems. As models become more complex, with more parameters and intricate architectures, they can achieve higher prediction accuracy when trained on larger datasets. However, despite the great prediction...