Data Science Commons^™

Material Costs, Karima Weinman

Masters Theses

This thesis investigates how migration fatality and disappearance data can be reinterpreted through material craft to create a more reflective encounter with information. Working with the Missing Migrants Project's dataset, this project asks how design can communicate dimensions of human loss that conventional data visualization cannot reach.

The work situates contemporary border violence within a longer colonial history, arguing that the logics of surveillance and quantification that structured European imperial expansion persist in the databases that govern mobility in the Mediterranean today.

Terrazzo is a 15th-century Venetian flooring technique built from discarded fragments bound together into a unified surface. This …

An Analytical Framework For Quantifying Urban And Community Resilience To Natural Hazards From Cell-Phone Gps-Location And Traffic-Flow Data, Georgios Chatzikyriakidis

Civil and Environmental Engineering Theses and Dissertations

Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.

A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of …

Bayesian Spatiotemporal Model For Counterfactual Estimation In Socioeconomic Studies, Duwani W. Gonzalez

Statistical Science Theses and Dissertations

Impact evaluations of regional development programs often require estimating counterfactual outcomes for a small number of treated regions using survey-based areal data. In practice, evaluators typically rely on two-group quasi-experimental methods such as propensity score matching (PSM) and Difference-in-Differences (DiD). These approaches perform poorly when only a few regions receive treatment, and when the set of observed covariates is limited or only partially relevant. Moreover, they typically do not explicitly exploit the spatial and temporal dependence present in survey-based areal data such as in ACS (American Community Survey). This dissertation develops a family of Bayesian spatial predictive models for directly …

Remote Sensing To Detect Crop Damage By An Invasive Species, Cole Butler

Biology and Medicine Through Mathematics Conference

No abstract provided.

Effectiveness Of The Guided Discovery Method In Teaching The Surface Area Of A Cylinder, Paul Ahortu

2026 Symposium

This study investigates the impact of the guided discovery instructional method on students’ understanding of the surface area of a cylinder. A quasi-experimental pre-test–post-test design was conducted with 100 senior high school students in Cape Coast, Ghana, divided into experimental and comparison groups..

Results showed a substantial improvement in performance for students exposed to guided discovery, with mean scores increasing from 1.25 (pre-test) to 9.43 (post-test) and a large effect size (Cohen’s d = 2.70). Statistical analysis also revealed significant gender differences in achievement.

These findings indicate strong improvement following the guided discovery intervention and suggest its potential to enhance …

Uncovering The Impact Of Youtube's Hidden Algorithm On Its Users, Oscar Perez

COD Library Student Research and Award Symposium

YouTube is a well-known platform that offers users endless hours of news, entertainment, and education. This research seeks to understand how the algorithm functions and uncover the effects of allowing a system to curate content for viewers. The research combines academic sources with fieldwork to understand the impact of YouTube's algorithm.

Faculty Sponsor:  Professor Jacqueline McGrath

Dynamic Trust Calibration, Bruno Miranda Henrique

Dartmouth College Ph.D Dissertations

Trust calibration between humans and Artificial Intelligence (AI) is crucial for optimal decision-making in collaborative settings. Excessive trust can lead users to accept AI-generated outputs without question, overlooking critical flaws, while insufficient trust may result in disregarding valuable insights from AI systems, hindering performance. Despite its importance, there is currently no definitive and objective method for measuring trust calibration between humans and AI. Current approaches lack standardization and consistent metrics that can be broadly applied across various contexts, and they don’t distinguish between the formation of opinions and subsequent human decisions. This thesis brings a novel and objective method for …

A Mathematical & Computational Study Of Voting Power In Social Choice Systems, Madison T. Gambon

Undergraduate Honors Theses

This thesis develops a computational framework for measuring the vulnerability of voting rules to coordinated strategic manipulation. While classical results show that most voting systems are theoretically manipulable, less is known about the magnitude of coordination required to alter outcomes or how that magnitude varies across institutional designs. I define the minimal manipulating coalition size k* as the smallest number of voters whose strategic ballot changes can overturn a sincere election outcome under a given rule. To enable cross-election comparison, I introduce the normalized manipulation threshold θ = k*/n . Using algorithmic search procedures and Monte Carlo simulation, I estimate …

Meeting The Moment With Ai-Employer Informed Education, Brent Terwilliger, John Faraca

Publications

As artificial intelligence transforms aviation, aerospace, and autonomy-related sectors, higher education must adapt to meet evolving workforce demands. This session shares emerging findings from a nationwide study led by Embry-Riddle Aeronautical University, focused on employer perceptions of AI adoption, responsible use, and workforce preparedness in domains including uncrewed systems, space systems, robotics, and advanced air mobility. Based on a structured survey and follow-up interviews, the presentation explores how organizations are using AI tools, from generative platforms to enterprise systems, and defining effective and inappropriate use in operational contexts. Participants will gain insight into critical concerns (e.g., data privacy, compliance, security, …

Labeling And Describing Objects In A Photogrammetry-Based 3d Environment Using Computer Vision And Artificial Intelligence, Colin Donald Moschella

Capstone Projects

Cataloging and digitizing the objects inside a building manually is a task that is often impractical at scale. This project therefore automates the process, using a custom-made system. Using a photogrammetry-based 3D reconstruction of a room, this system is applied to sequences of 2D images used to make the 3D models. The system applies object detection, image segmentation, and image-text models to identify and describe objects, using CNN based models such as YOLO and OpenCLIP. Each analyzed object is then stored in a structured database with spatial coordinates from the 3D scanning, descriptive attributes from the image-text models, and other …

Stridevision: Automated Detection Of Running Form Deviations From 2d Pose Estimation And Machine Learning, Paulina Eguibar Ortega

Honors Theses

Running gait analysis plays a critical role in injury prevention and performance optimization, however, existing approaches often rely on specialized laboratory equipment or wearable sensors with limited interpretability. Recent advances in computer vision, particularly 2D human pose estimation, enable markerless motion analysis from standard video. However, progress remains constrained by the lack of publicly available datasets designed for running form analysis.

In this work, we introduce a preliminary dataset and benchmark for stride-level running gait analysis. The dataset consists of 73 treadmill running videos from 15 participants with varying experience levels, annotated with over 4,600 stride-level labels across multiple biomechanical …

Assessing Trends In Medical Students’ Perceptions Regarding Statistical Analysis, Ethan Noble, Valeriy Kozmenko, Paul Thompson

Scholarship Pathways Program

Assessing Trends in Medical Students’ Perceptions Regarding Data Analysis and Statistics Knowledge and Skills

Ethan Noble, MD | Mentors: Valeriy Kozmenko, MD, Paul Thompson, PhD

Introduction: The use of evidence-based medicine requires that physicians are able to properly analyze and interpret the results of new research. The development of new research and medical knowledge is swift, and a strong foundation in statistics and research is needed for physicians and medical students to keep up with new research. Curriculum in medical education often lacks in-depth coverage of the subject, and additional curriculum has been shown to enhance student confidence and ability …

Freshman 15? Freshman 50?? The Reality Of Daily Life Habits Of A First Year College Student, Gregorio R. Salgado

Student Scholar Symposium Abstracts and Posters

This project presents a personal data tracking study in which I collected daily self-reported metrics over the course of the Spring semester using Excel. The variables tracked include sleep duration, caloric intake, screen time, social media usage, phone checks per day, family communication, and personal spending. The goal of this project is to identify meaningful patterns and correlations between daily habits and personal well-being.

Data was collected through a combination of manual logging and smartphone-generated daily reports. This study explores potential relationships between variables such as sleep duration and social media usage, as well as the association between family communication …

Improving Semantic Precision In Text-To-Image Diffusion Models Via Latent-Space Optimization And Semantically-Parsed Evaluation, Mohammad Rouie Miab

McKelvey School of Engineering Graduate Student Theses & Dissertations

Text-to-image diffusion models can produce visually impressive images from natural-language prompts, but they often fail to satisfy the detailed semantic constraints expressed in compositional prompts. Typical failure modes include omitted objects, merged entities, incorrect quantities, incorrect attribute binding, and leakage of one entity's attributes onto another. This thesis studies the problem of semantic precision in text-to-image generation: how faithfully a generated image satisfies the structured meaning of its prompt.   The thesis makes two linked contributions. First, it presents a training-free inference-time refinement method for diffusion-based image generation. The method operates directly in latent space during denoising and uses noun-phrase-aware cross-attention …

Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool, Thomas Kimmeth, Avijit Roy, Vivek Sharma

Publications and Research

Propagandistic content increasingly circulates through online news and social media, where readers often encounter it with limited scrutiny, highlighting the need for reliable and fine-grained detection. This paper introduces Propasafe-Hybrid, a sentence-level system that integrates a fine-tuned transformer classifier with LLM-based technique classification to identify, label, and explain specific propaganda strategies. The pipeline generates actionable outputs, including highlighted sentences, technique assignments, and concise rationales, so users can immediately understand why a sentence was flagged and how each label was determined. To control inference cost, Propasafe-Hybrid employs a cost-aware pre-filtering stage that forwards only high-likelihood sentences to LLMs, reducing token usage …

Evaluation In Applied Ai: Predictive Modeling, Agent Safety, And Model Validation, Tanmay Sah

Harrisburg University Dissertations and Theses

This dissertation focuses on three applied challenges in machine learning and artificial intelligence: predicting social media content virality, evaluating safety in tool using large language model agents, and standardizing model validation in regulated settings. Across these domains, the dissertation argues that evaluation in applied AI must be matched to context, combining predictive performance with the relevant evidence needed for interpretability, safety, reproducibility, and governance readiness. First, Decoding Reddit Memes Virality extracts and analyzes 16,968 posts using computer vision, natural language processing, and gradient boosting to identify visual, textual, and temporal signals associated with virality. Second, The Verifier Tax designs and …

The Impatience Of Winning: An Analysis Of Time Discounting, Predictive Modeling, And The Nba Draft, Alec R. Plante

Business and Economics Honors Papers

This paper examines whether NBA draft decisions can be better explained by incorporating non-geometric time discounting into a model of general manager decision making. Using a dataset of 285 NBA draft prospects over a 12-year period, the impact of college statistics on Value Over Replacement Player (VORP) is determined, and these impact values are then used to create a “predicted” VORP for the first 4 seasons of each player’s career: a projection of what a general manager might think of a prospect’s future value given their college statistics. Following this, geometric and hyperbolic time discounting models are applied to estimate …

Fossil-Fuels In A Decarbonized Country? Modeling The Drivers Of Icelandic Oil Sales, Inbal Armony

Environmental Studies Honors Projects

Although 100% of Iceland’s electricity comes from renewable energy sources, it still relies on fossil fuels for land transportation, marine transportation, aviation, and some industry. Understanding geographic nuances in oil use is critical to achieving Iceland’s goals of carbon neutrality by 2040. As the island has one primary urban center with two thirds of the population, information is lacking about oil use in non-Capital areas and a gap between state and municipal climate plans. Using newly available data of oil sales at the municipality-level in a Small Area Estimation model, we analyze drivers of oil sales across Icelandic municipalities. We …

Augmented Reality In Fashion Retail: A Walmart Unlimited Study, Chloe A. Mcpherson

Apparel Merchandising and Product Development Undergraduate Honors Theses

As technology continues to evolve, augmented reality (AR) has become increasingly common within the retail and fashion industries. This study explored Gen Z consumers’ perceptions of immersive AR shopping experiences through Walmart Unlimited, an interactive digital shopping platform. The purpose of this research was to better understand how younger consumers respond to AR-enhanced shopping environments and whether these technologies influence attitudes toward convenience, engagement, and sustainability in retail.

A quantitative research design was used for this study. Participants completed the Walmart Unlimited shopping experience and then responded to a Qualtrics survey measuring areas such as immersion, satisfaction, ease of use, …

Strengthening Cyber Resilience In Critical Infrastructure: Lessons From Major Attack Case Studies, Jodi Barnes

Data Science Undergraduate Honors Theses

This comparative case study research paper analyzes the Colonial Pipeline attack, the Oldsmar Water Treatment Plant attack, and related case studies to identify past and current gaps in cyber resilience in critical infrastructure.  It provides insights into the importance of cybersecurity and opportunities to enhance protection in an increasingly digital world.  Findings include unsecure practices, limited communication between sectors, outdated technology, and weaknesses in security processes and employee training. These vulnerabilities are interconnected and are largely driven by limited funding within critical infrastructure systems, which restricts the ability to address them effectively.

Reimagining Less-Than-Truckload Pricing Development In Competitive Bid Environments With Artificial Intelligence, Lawson C. Levin

Data Science Undergraduate Honors Theses

This undergraduate thesis explores how data analytics and engineering judgment are used to support pricing decisions in the less-than-truckload (LTL) freight market. It’s based on an internship with ArcBest Corporation. It explains the company’s background, its role in the LTL market, and the responsibilities of a Pricing and Supply Chain Engineer within the Yield department.

Most of the internship was spent evaluating requests for proposals (RFPs), in which a negotiating third party provides a customer’s shipment data that must be cleaned, analyzed, and translated into a comprehensive pricing offer. Using the Data Science Analytics Process as a framework, this thesis …

Prescribing Company Action Through Machine Learning And Ai, Breck T. Husong

Data Science Undergraduate Honors Theses

The purpose of this research is to implement an OpenAI Reinforced Learning prescription-giving model for improving sales on a week-by-week basis. The data used comes from a segment of High Impact Analytics’s sales data that has been anonymized for proprietary reasons. The features among the data include inventory numbers, shipments in transit, total quantity and dollars of products sold each week for the past 2 years, all aggregated at the store-item-week level. In order to build this model, Tigramite, a causal discovery model combined with prediction models XGBoost, Linear Regression, Ridge Regression, Lasso Regression, Scikit-learn’s MLP, and Keras’s Neural Model …

A Comparative Machine Learning Framework For Identifying Ai-Generated Versus Real Celebrity Faces, Sidney Gehring

Data Science Undergraduate Honors Theses

The rapid advancements in the world of generative artificial intelligence has enabled the creation of highly realistic fictitious facial images, raising concerns about authenticity and bias in computer vision systems. This study investigates the capabilities of machine learning models to distinguish between real and artificially generated facial images across gender and race focusing on celebrity imagery. Four datasets were used against the classification model, each trained on images of a single celebrity within distinct demographic groups: White women, White men, Black women, and Black men. For each group, real images are paired with AI-generated counterparts designed to closely replicate the …

A Forecasting Framework For Distribution Center Capacity Utilization: An Applied Industry Study, Jordan J. Shortt

Data Science Undergraduate Honors Theses

This project develops and evaluates a predictive modeling framework for forecasting distribution center capacity utilization at Company Y, with monthly forecast horizons up to one year. Motivated by the operational challenges of seasonal demand volatility, promotional cycles, and the absence of a formally defined capacity metric, the study first constructs a historical capacity utilization measure from raw warehouse management system data — reconciling item volumes, location dimensions, and utilization factors across all DCs — which serves as the target variable for all modeling work. Four models are developed and evaluated against a naïve seasonal baseline: SARIMA, LightGBM, LSTM, and a …

Developing Tracking Compliance Standards For Inbound Freight: A Data-Driven Industry Application At O’Reilly Automotive, Jackson Endacott

Data Science Undergraduate Honors Theses

Visibility of inbound freight is critical for managing operational efficiency, yet many organizations lack standardized compliance metrics for third-party carriers to uphold, preventing them from utilizing tracking data to make data-driven decisions. During a summer internship with the Transportation Department at O’Reilly Automotive, data inconsistencies were addressed in the Transportation Management System (TMS), and that data was utilized to create tracking compliance standards for third-party carriers. Data populated from various sources within O’Reilly’s TMS was cleaned, validated, and utilized to create a Tracking Scorecard that evaluates message transmission rates, timeliness, and errors. This tool provides actionable insights to improve tracking …

Pyspqr: A Python Package For Density Estimation Using Deep Learning, Cameron Eddy, Reetam Majumder

Electrical Engineering and Computer Science Undergraduate Honors Theses

Splines are used for representing complex functions. In statistics, splines can be used for distributional shapes that are difficult to model by traditional parametric approaches. Ramsay (1) uses M-Spline bases to estimate continuous distributions. Semi-Parametric Quantile Regression (SPQR), developed by Xu and Reich (2), models conditional distributions where a neural network is used to estimate the basis function weights that depend on covariates. (3) implements a package for SPQR in R. We build on this by implementing a version of SPQR in Python with PyTorch. By using PyTorch, we can use more sophisticated deep learning architectures than those available in …

High Throughput Phenomics Pipeline For Pulse Crop Nutritional Breeding, Amod Udayanga Madurapperumage

All Dissertations

Dry pea (Pisum sativum L.), lentil (Lens culinaris Medik.), and chickpea (Cicer arietinum L.) are major pulse crops valued for their high nutritional composition and importance to global food systems. Pulses are rich in carbohydrates, protein, and essential minerals, making them ideal whole foods and critical contributors to food and nutrition security. Due to these advantages, pulse breeding programs are increasingly focusing on enhancing nutritional traits, such as protein quality, amino acid balance, and micronutrient density, through the process of biofortification. However, improvement of agronomic traits remains equally essential. Characteristics such as plant height, standability, stress tolerance, …

Modular Category Optimization For Substitutability: An Item-Level Approach, Medhansh A. Sankaran

Data Science Undergraduate Honors Theses

This thesis examines substitutability within Walmart apparel as a foundation for modular category optimization. Using large-scale item-level data, I develop an attribute-based framework that aggregates products to the fineline level, constructs a structured feature space, and identifies candidate substitute relationships through similarity-based matching within relevant merchandise groupings. The results show that Walmart item master data contains sufficient structure to support scalable substitute generation across a high-variety assortment. However, substitutability is not uniform: many item pairs exhibit high similarity but low observed demand transfer, indicating that structural similarity alone does not guarantee substitution. To address this, the framework is positioned within …

Recursion, Regurgitation, And Regeneration: Testing Limits And Revealing Biases Of Generative Ai Models Through Multimodal Feedback Loops, William Donnell-Lonon

Data Science Undergraduate Honors Theses

Contemporary generative AI systems such as OpenAI's GPT-4o and DALL-E models embed complex priors about society, reality, and history shaped by training data distributions, social alignment procedures, legal constraints, and safety regulations. This study uses a "telephone game" methodology to investigate how embedded social, political, and visual biases propagate and reveal themselves through iterative multimodal generation loops, where image captioning and text-to-image models are chained in successive feedback cycles.

Using CLIP similarity metrics, facial recognition algorithms, semantic drift analysis, and qualitative content observations, I tested how image subject matter affects the rate and quality of semantic and visual shift, identity …

Escaping The Promotion Trap: A Machine Learning Framework For Brand Equity Preservation In Beverage Cpg, Lucas P. Jones

Data Science Undergraduate Honors Theses

When companies acquire beverage brands, they typically value them based on total sales revenue. This traditional approach treats all sales equally over time, whether they are driven by genuine consumer demand or temporary discounts. This is important because while promotions can boost short-term sales, they tend to erode brand value over long periods of time. The measurement problem extends to acquisitions, where buyers lack the tools to distinguish real consumer demand from artificial promotional inflation.

This thesis develops a framework to separate genuine baseline demand from promotional dependence using Nielsen scanner data covering 189 beverage brands across 188,304 weekly observations …