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DescriptionThis project tracks the history of fiscal stimulus in the United States as it relates to defense and economic projects. This is done in order to place the Biden administration's fiscal agenda into a historical context of fiscal spending.
ContributorsMiller, Jordan (Author) / Calhoun, Craig (Thesis director) / Kirkpatrick, Jennet (Committee member) / Fong, Benjamin (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor)
Created2024-05
Description
The compelling question is “How can a sense of belonging be brought to the Math classroom?” This topic centers at the intersection of mathematics, history, and education. The mathematics field is overwhelmingly portrayed as antiquated, white, and male. There is a lack of history taught in the math classroom. As such, adding history that counters the perceptions of who belongs in mathematics will engage students who had not previously felt represented in the field. In addition, students who lack interest in mathematics may find themselves interested in the historical aspects, leading to more retention in the classroom. The main goals of this project are to create an addition to the mathematical curriculum that can be added as a starter to classroom discussions or a warm up before a lecture. The ideal form is a laminated flip book consisting of photos, descriptions, and fun facts about 52 mathematicians (one per week) from diverse time periods, countries, and backgrounds.
ContributorsNeff, Juniper (Author) / Klemaszewski, James (Thesis director) / Mohacsy, Hedvig (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Public Affairs (Contributor)
Created2024-05
Description
The performance of modern machine learning algorithms depends upon the selection
of a set of hyperparameters. Common examples of hyperparameters are learning
rate and the number of layers in a dense neural network. Auto-ML is a branch
of optimization that has produced important contributions in this area. Within
Auto-ML, multi-fidelity approaches, which eliminate poorly-performing configurations
after evaluating them at low budgets, are among the most effective. However, the
performance of these algorithms strongly depends on how effectively they allocate
the computational budget to various hyperparameter configurations. We first present
Parameter Optimization with Conscious Allocation 1.0 (POCA 1.0), a hyperband-
based algorithm for hyperparameter optimization that adaptively allocates the inputted
budget to the hyperparameter configurations it generates following a Bayesian sampling
scheme. We then present its successor Parameter Optimization with Conscious
Allocation 2.0 (POCA 2.0), which follows POCA 1.0’s successful philosophy while
utilizing a time-series model to reduce wasted computational cost and providing a
more flexible framework. We compare POCA 1.0 and 2.0 to its nearest competitor BOHB
at optimizing the hyperparameters of a multi-layered perceptron and find that both
POCA algorithms exceed BOHB in low-budget hyperparameter optimization while
performing similarly in high-budget scenarios.
ContributorsInman, Joshua (Author) / Sankar, Lalitha (Thesis director) / Pedrielli, Giulia (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
If quantum computing becomes feasible, many popular cryptographic schemes, such as RSA, Diffie-Helman, and methods using elliptic curves will no longer be secure. This paper explores code-based cryptography, specifically looking the McEliece cryptosystem, as well as the more recent Classical McEliece cryptosystem, which was proposed to the National Institute of Standards and Technology (NIST) as a potentially quantum-secure algorithm.
ContributorsFletcher, Julia (Author) / Sprung, Florian (Thesis director) / Childress, Nancy (Committee member) / Barrett, The Honors College (Contributor) / School of Social Transformation (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor)
Created2024-05
DescriptionBuck-It is a budgeting application designed to meet the unique needs of college students. As financial literacy is crucial for developing good long-term financial habits, Buck-It aims to promote budgeting among college students through an appealing user interface, robust customization, and effective categorization.
ContributorsDoyle, Michael (Author) / Davitt, Ryan (Co-author) / Walle, Andrew (Co-author) / Vemuri, Rajeev (Co-author) / Baptista, Asher (Co-author) / Byrne, Jared (Thesis director) / Lee, Peggy (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
Through personal experience, my co-founder and I know that young adults often get wrapped up in their work and can forget about the key aspects of life like friendship, mental and physical health, and going to an exciting event. These issues are particularly prevent when someone is planting roots in a new city. In order to form a solution to this daunting feeling of unfamiliarity and disconnectedness, we have created The Loop which is an app that aims to combat these problems in young adults’ lives. This app provides a platform for young adults all around their designated city to find comfort in small communities. We gather data of the user’s interests and they then are presented a wide variety of events and “loops” to join that cater to their preferences. With this app, we hope that young adults can find their home away from home and we will provide that for them by keeping them in the loop.
ContributorsCavalier, Mia (Author) / Cote, Jillian (Co-author) / Byrnes, Jared (Thesis director) / Swader, Melissa (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
For decades, society has held an innate fascination with serial murder and serial killers. The fascination lies in the motivations behind the actions and the way in which investigators apprehend them. The psychological field of investigative and behavioral psychology emerged to attempt to answer some of these questions and the investigative tool of behavioral profiling soon followed. Researchers have conducted comparison studies of male and female serial killers many times to understand what differentiates them. This research aims to answer another question: Are female serial killers more homogenous based on their profiles than male serial killers? The media portrays female serial killers in a very specific light, poisoners who kill due to revenge or money, but how well does this portrayal actually hold up when analytically examined? This research compiled case studies of fifteen male and fifteen female serial killers based on twenty-six characteristics and profiled each according to three different typologies to determine how homogenous these groups actually are. This research can help assist investigators and the public to better understand the diversity of these types of offenders and be able to determine who these offenders are.
ContributorsRotenberg, Taylor (Author) / Guyll, Max (Thesis director) / Madon, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Department of Marketing (Contributor) / Department of Finance (Contributor) / Economics Program in CLAS (Contributor)
Created2024-05
Description
Milk has long played an important role in American society and remains a popular staple of many Americans’ diets. Yet, despite its long standing popularity, the role of milk within American society has begun to develop new meaning in recent years. This paper aims to understand the symbolism that today’s Americans ascribe to milk. Academic journal articles, advertising campaigns, online articles, and government policy pertaining to milk were researched in order to identify the themes that characterize consumers’ perceptions of milk. In recognition of the diverse types of milk that are now accessible to many Americans, this paper uses the word “milk” to refer to cow-derived, fluid (liquid) dairy unless otherwise specified. This research reveals eleven principal themes that describe consumers’ perceptions of milk: milk symbolizes health, American values, is associated with athleticism, is unhealthy, is not preferable to plant-based alternatives, is bad for the environment, is animal cruelty, represents white supremacy, is anti-feminist, is reflective of consumer lifestyles, and there is a general trend of consumers being uninformed about the milk that they consume. This research helps to understand consumers; therefore, this research can be used to help dairy-related industries shape their business strategies and target their customer segment and to help policymakers design effective dairy-related policies. Furthermore, this paper invites further research to identify the consumers that hold the beliefs this research describes, and the extent to which these consumers share said beliefs.
ContributorsHladik, Jessica (Author) / Hughner, Renee (Thesis director) / Voorhees, Matthew (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Economics Program in CLAS (Contributor) / Dean, W.P. Carey School of Business (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05
Description
This project is a policy analysis of Medical-Legal Partnerships (MLP). There is a serious lack of civil legal help for low income individuals in America. One way to address this issue is incorporating legal care into medical care through an MLP, which provides free legal help to address patients’ Social Determinants of Health. This thesis advocates for the incorporation of MLPs into healthcare, as well as more research into the health benefits if an MLP.
ContributorsBrock, Riley (Author) / Kizer, Elizabeth (Thesis director) / Helitzer, Deborah (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Politics and Global Studies (Contributor)
Created2024-05
Description
This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and extreme gradient boosting, to predict the outcomes of soccer matches in the EPL. Utilizing a comprehensive dataset from Kaggle, the study uses the Sport Result Prediction CRISP-DM framework for data preparation and model evaluation, comparing the accuracy, precision, recall, F1-score, ROC-AUC score, and confusion matrices of each model used in the study. The findings reveal that ensemble methods, notably Random Forest and Extreme Gradient Boosting, outperform other models in accuracy, highlighting their potential in sports analytics. This research contributes to the field of sports analytics by demonstrating the effectiveness of machine learning in sports outcome prediction, while also identifying the challenges and complexities inherent in predicting the outcomes of EPL matches. This research not only highlights the significance of ensemble learning techniques in handling sports data complexities but also opens avenues for future exploration into advanced machine learning and deep learning approaches for enhancing predictive accuracy in sports analytics.
ContributorsTashildar, Ninad (Author) / Osburn, Steven (Thesis director) / Simari, Gerardo (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05