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Analysis of large network datasets has become increasingly important. Algorithms have been designed to find many kinds of structure, with numerous applications across the social and biological sciences. However, a tradeoff is always present between accuracy and scalability; otherwise promising techniques can be computationally infeasible when applied to networks with huge numbers of nodes and edges. One way of extending the reach of network analysis is to sparsify the graph by retaining only a subset of its edges. The reduced network could prove much more tractable. For this thesis, I propose a new sparsification algorithm that preserves the properties of a random walk on the network. Specifically, the algorithm finds a subset of edges that best preserves the stationary distribution of a random walk by minimizing the Kullback-Leibler divergence between a walk on the original and sparsified graphs. A highly efficient greedy search strategy is developed to optimize this objective. Experimental results are presented that test the performance of the algorithm on the influence maximization task. These results demonstrate that sparsification allows near-optimal solutions to be found in a small fraction of the runtime that would required using the full network. Two cases are shown where sparsification allows an influence maximization algorithm to be applied to a dataset that previous work had considered intractable.

Lung cancer (LC) is the leading cause of cancer death in the US, claiming over 160,000 lives annually. Although CT screening has been shown to be efficacious in reducing mortality, the limited access to screening programs among high-risk individuals and the high number of false positives contribute to low survival rates and increased healthcare costs. As a result, there is an urgent need for preventative therapeutics and novel interception biomarkers that would enhance current methods for detection of early-stage LC. This thesis addresses this challenge by examining the hypothesis that transcriptomic changes preceding the onset of LC can be identified by studying bronchial premalignant lesions (PMLs) and the normal-appearing airway epithelial cells altered in their presence (i.e., the PML-associated airway field of injury). PMLs are the presumed precursors of lung squamous cell carcinoma (SCC) whose presence indicates an increased risk of developing SCC and other subtypes of LC. Here, I leverage high-throughput mRNA and miRNA sequencing data from bronchial brushings and lesion biopsies to develop biomarkers of PML presence and progression, and to understand regulatory mechanisms driving early carcinogenesis. First, I utilized mRNA sequencing data from normal-appearing airway brushings to build a biomarker predictive of PML presence. After verifying the power of the 200-gene biomarker to detect the presence of PMLs, I evaluated its capacity to predict PML progression and detect presence of LC (Aim 1). Next, I identified likely regulatory mechanisms associated with PML severity and progression, by evaluating miRNA expression and gene coexpression modules containing their targets in bronchial lesion biopsies (Aim2). Lastly, I investigated the preservation of the PML-associated miRNAs and gene modules in the airway field of injury, highlighting an emergent link between the airway field and the PMLs (Aim 3). Overall, this thesis suggests a multi-faceted utility of PML-associated genomic signatures as markers for stratification of high-risk smokers in chemoprevention trials, markers for early detection of lung cancer, and novel chemopreventive targets, and yields valuable insights into early lung carcinogenesis by characterizing mRNA and miRNA expression alterations that contribute to premalignant disease progression towards LC. / 2020-01-25

Identity theory (IT) is a sociological theory that helps to explain how societal patterns and norms shape the ways in which people behave and make decisions. The current project presents a comprehensive exploration of IT in the context of academic conferences, shedding light on the multifaceted identities of sociologists as scholars, educators, activists, and beyond. It examines how these diverse roles intersect and influence behaviors within professional settings. The first article critiques traditional IT research's limitations and adopts a qualitative approach to more accurately capture how participants describe themselves, moving beyond the constraints of previous methodologies. The second piece investigates homophily–the tendency to associate with similar others. Focusing on minority identities in higher education, this study explores homophily across various demographics, such as race, gender, and academic rank, thus providing insights into the nuances of inequality within academic circles. The final article examines the impact of technology in academic conferences, particularly in the post-COVID-19 era. It analyzes how oppressed identities leverage a conference mobile app for networking, highlighting technology's role in creating inclusive environments and enhancing connections among marginalized groups. Collectively, this dissertation offers a nuanced view of identity within the academic sphere. By challenging existing IT research paradigms, introducing innovative survey techniques, linking IT with homophily, and assessing technology's influence on conference dynamics, this work enriches our understanding of sociologists' identities and interactions. It holds significant implications for future research and the development of more equitable and inclusive sociological communities, emphasizing the complex interplay of personal and professional identities in academic settings. / Doctor of Philosophy / This project looks at how sociologists, who are not just researchers but also teachers, activists, and more, understand and express their different roles, especially at academic conferences. It explores how these various roles affect the way they act in professional environments. The first part of the study questions the usual ways of studying this topic and tries a new method to get a deeper understanding of how people see themselves. The second part looks at how people often prefer to connect with others who are like them, focusing on how this plays out among different races, genders, and job levels in universities, shedding light on hidden inequalities. The last part examines how technology, especially after COVID-19, is used in academic conferences. It looks at how people who often face challenges or discrimination use a conference app to network, showing how technology can help make these events more welcoming and useful for everyone. Overall, this research gives us a richer picture of how sociologists balance their personal and professional lives. It challenges old ways of thinking, introduces new research methods, and shows how technology affects professional gatherings. This is important for making the field of sociology more inclusive and understanding the complex ways people interact in academic settings.

A reconfiguration algorithm for electrical distribution system to reduce system losses is presented. The algorithm determines the switching patterns as a function of time. Either seasonal or daily time studies may be performed. Both manual and automatic switches are used to reconfigure the system for seasonal studies, whereas only automatic switches are considered for daily studies. An algorithm for load estimation is developed. The load estimation algorithm provides load information for each time point to be analyzed. The load estimation algorithm can incorporate any or all of the following: spot loads, circuit measurements, and customer time-varying diversified load characteristics. Voltage dependency of loads is considered at the circuit level. It is shown that switching at the system peak can reduce losses but may cause a marginal increase in system peak. Voltage and current constraints are incorporated in the reconfiguration algorithm. Data base tables and data structures used in the algorithm are described. Example problems are provided to illustrate results. / Ph. D.

The protective design industry has to adapt to new threats and challenges facing the industry constantly. As a result, invested stakeholders within the industry must take a critical look at the current state of the practice. By assessing the current protective design industry, one can identify both challenges and opportunities within it and provide insight into how to improve the industry. This study aims to understand the current state of the protective design industry through an analysis of protective design literature and interviews with protective designers. Both academic literature (conference papers and journal articles) and design guidelines showcase the current trends and challenges within the industry. While understanding the protective designer's perception of their role help explain how protective designers engage within the design process with other design stakeholders. Together, both the literature and the people will dictate the current state of the protective design industry. Lastly, this study has developed a database for protective design guidelines that both protective designers and other design stakeholders can utilize to search for a comprehensive database. / M.S. / The 2001 September 11th attacks fundamentally changed the protective design industry. Not only did it take the lives of thousands of Americans, but it showcased a flaw in our national security. Designers and engineers had to rethink their perspectives on security and proceed to integrate more protective measures in both the private and commercial sectors of design. Now, nearly two decades later, there hasn't been a deadly attack to the scale of 9/11, but new threats are facing the protective design industry. Newer and more recurring threats such as mass shootings within the United States and vehicle attacks have become a significant threat. Because of these new threats facing the industry, it is appropriate to take a critical look at the challenges and trends in the protective design industry that need improvement. This study aims to understand the current state of the practice in the protective design industry by reviewing both the protective design literature and interviewing protective designers.

Technological advancements in battery electric vehicles (BEVs) have developed alongside increases in vehicle size and the introduction of vehicle styling more similar to internal combustion engine vehicles (ICEVs). Increases in the distance a BEV can travel on a single charge have been accompanied by the ability to recharge the vehicle much faster than the BEV models available just 10 years ago. The Environmental Protection Agency (EPA) reports for model year 2021 include 40 BEV models and many manufacturers have signaled plans to increase the number of battery electric vehicle models offered. As more consumers consider purchasing a battery electric vehicle the question of how well that vehicle can meet all their needs is asked more frequently. This research examines the current DC-Fast charging infrastructure to evaluate how the current distribution of chargers impacts consumer convenience for non-commute routes. No study has evaluated the impact that the current DC-Fast charging infrastructure has on the consumer driving experience and we fill this research need because it will allow consumers to understand more accurately how a (BEV) may meet their needs while also allowing BEV manufacturers to better understand the impacts of potential investments in charging infrastructure. The authors examine over 30,000 pairs of simulated BEV and ICEV routes and compare the distance and duration variations for each pair. Due to our effort to consider the suitability for long distance trips, we have ensured that more than 50% of the simulated routes have a minimum travel distance of 500 miles and over 15% of the routes exceed 1000 miles. Working from this data, 99.7% of the locations in a sample of 360 places in the contiguous U.S. can be reached without relying on the ability to charge a BEV overnight. We further identify a median increase in BEV trip duration of 13.1% and a median increase in distance of 0.06%. The differences in median travel time, particularly when trips exceed 400 miles suggests that long trips made with a BEV may result in longer total travel time, however, differences in route length between BEVs and ICEVs were minimal. These findings serve as the foundation to discuss challenges and solutions related to widespread non-commuter adoption of BEVs in a variety of geographic locations, including how and where the consumer experience may vary. The results from this work will support consumer awareness about the ability of a BEV to meet their needs as well to aid in the evaluation of infrastructure investment as it relates to improving the consumer experience. The methods employed serve as a foundation for future work to investigate the relationship between vehicle type and consumer experience as well as to advance algorithms capable of evaluating routes that require a selection to be made from a set of optional stops. / Master of Science / Technological advancements in battery electric vehicles have developed alongside increases in vehicle size and the introduction of vehicle styling more similar to the gasoline powered internal combustion engine vehicles that many people currently own. Increases in the distance a vehicle can travel on a single charge have been accompanied by the ability to recharge the vehicle much faster than the battery electric vehicle models available just 10 years ago. The Environmental Protection Agency reports that there are 40 battery electric vehicle models available for model year 2021 and many manufacturers have signaled plans to increase the number of battery electric vehicle models offered. As more consumers consider purchasing a battery electric vehicle the question of how well that vehicle can meet all their needs is asked more frequently. This study examines one of the factors that impact the answer to that question: how does the driving experience vairy between gasoline powered vehicles and battery electric vehicles when long trips must be made. The distance and total time to complete the trips were compared across more than 30,000 pairs of routes within the lower 48 states of the United States and the District of Columbia. Battery electric vehicle routes were modeled based on the capabilities of Tesla vehicles due to the well-developed charging infrastructure that supports them. More than 50% of the routes examined exceed 500 miles, emphasizing the focus on long distance travel. Many routes with a total length of less than 400 miles were found to have little or no difference in total travel time or travel distance. However, when trips with a length of 500 miles or more are included the median difference in travel time is 13.1% accompanied by a minimal difference in travel distance of 0.06%. Due to the rapidly increasing travel range of battery electric vehicles and the speed at which they can recharge combined with the frequent installment of new charging locations throughout the United States it is expected that these differences would be smaller today than at the time this study was conducted. The results of this study can be used by consumers to establish realistic expectations regarding how the experience of traveling long distances in a battery electric vehicle may compare with the gasoline powered vehicle they are already familiar with. Battery electric vehicle manufacturers and others considering investments in charging infrastructure may also apply the findings discussed in this study to better communicate the long-distance performance of their vehicles with consumers and identify locations where improvements in the charging infrastructure would be most beneficial to the consumer experience. Future work is needed to explore how the long-range travel experience has continued to improve. The framework of this study provides a foundation for further evaluation of the impact that vehicle and infrastructure developments may have on the consumer experience.

This thesis represents a synthesis of relational learning and causal discovery, two subjects at the frontier of machine learning research. Relational learning investigates algorithms for constructing statistical models of data drawn from of multiple types of interrelated entities, and causal discovery investigates algorithms for constructing causal models from observational data. My work demonstrates that there exists a natural, methodological synergy between these two areas of study, and that despite the sometimes onerous nature of each, their combination (perhaps counterintuitively) can provide advances in the state of the art for both. Traditionally, propositional (or "flat") data representations have dominated the statistical sciences. These representations assume that data consist of independent and identically distributed (iid) entities which can be represented by a single data table. More recently, data scientists have increasingly focused on "relational" data sets that consist of interrelated, heterogeneous entities. However, relational learning and causal discovery are rarely combined. Relational representations are wholly absent from the literature where causality is discussed explicitly. Instead, the literature on causality that uses the framework of graphical models assumes that data are independent and identically distributed. This unexplored topical intersection represents an opportunity for advancement --- by combining relational learning with causal reasoning, we can provide insight into the challenges found in each subject area. By adopting a causal viewpoint, we can clarify the mechanisms that produce previously identified pathologies in relational learning. Analogously, we can utilize relational data to establish and strengthen causal claims in ways that are impossible using only propositional representations.