Data Science Commons^™

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

Human-Driven, Autonomous, Or Hybrid? The Optimal Fleet Configurations For Ride-Hailing Platforms, Wenjing Li, Yali Zhang, Jun Sun, Zhaojun Yang

Information Systems Faculty Publications

The growing commercialization of autonomous vehicles (AVs) is reshaping consumer service preferences and prompting ride-hailing platforms to redesign fleet structures that accommodate the coexistence of human-driven vehicles (HVs) and AVs. This article develops a queueing game framework that incorporates vehicle heterogeneity and consumer preference differences to systematically compare three fleet configuration strategies: the pure HV (PHV) strategy (HVs only), the pure AV (PAV) strategy (AVs only), and the hybrid strategy (both HVs and AVs). The analysis highlights how consumer mismatch losses, AV operating costs, and service rates jointly shape equilibrium outcomes. Results show that when consumer mismatch losses are moderate, …

Cdt-1d Cnn Integration With Simpson-Sobolev Regularization For High-Frequency Options Trading: With Fem-Based Heston Option Pricing, Daniel M. Margolis, Johannes Tausch, Arthur K. Selender

Mathematics Theses and Dissertations

This dissertation presents a computational framework for high-frequency options trading that combines Cross-Data-Type 1-D Convolutional Neural Networks (CDT-1D CNN) with Simpson-Sobolev regularization for directional prediction, and finite element methods (FEM) for realistic option pricing during backtesting. The core innovation lies in developing a mathematically rigorous regularization approach that maintains the adaptability of modern deep learning while enabling accurate evaluation through stochastic volatility models. The primary contribution is the Simpson-Sobolev regularization scheme, which extends traditional Sobolev regularization by incorporating Simpson’s rule for numerical integration. This approach achieves higher-order accuracy in approximating the Sobolev norms that control function smoothness. Simpson’s rule attains …

Ai For Regression Analysis And More, Eli Snir

Generative AI Teaching Activities

Students use Copilot and NotebookLM to create a dataset and develop statistical analyses including regression.

Measuring Stock Market Inefficiency Using A Multilayer Composite Efficiency Index: A Case Of The Egyptian Exchange, Patrick K. Owido, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

Financial markets play a critical role in resource allocation. Their performance depends on the decisions of millions of independent investors constantly reacting to one another. Their informational efficiency remains a subject of debate across economic systems. When informational efficiency is present at the weak form, historical price information should not consistently predict future returns. Several empirical tests of this hypothesis often focus on the behavior of aggregate market indices, and use individual efficiency proxies such as autocorrelation, GARCH-type volatility, or entropy-based measures to measure efficiency. This has often yielded mixed results, particularly in emerging markets. Here we show that testing …

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, …

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 …

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, …

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 …

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 …

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 …

Developing Strategies For Pce Outreach, Mariah Blankenbaker, Gordon Carlson, Daniel Adesoji, Levi Eck

SACAD: Scholarly Activities

In collaboration with Professional and Continuing Education (PCE), we produced projects to automate their internal tasks, as well as to promote their services. By taking advantage of software techniques, we bridged live-action footage with 2D and 3D computer visuals for promotional material. In addition, we researched the capabilities of creating a custom Generative Pre-trained Transformer (GPT) and trained it to analyze and interact with thousands of industry datapoints.

Moneyup: A Predictive Financial Management System For College Students, Isaiah J. Adams, Malaya E. Wilburd, Dan V. Le, Joshua P. Golden

ATU Scholars Symposium

College students often lack accessible tools that combine real-time financial tracking, mobile accessibility, predictive analytics, and secure system design, leaving many without structured insight into their spending behavior. MoneyUP is a full-stack financial management platform developed to address these challenges through a secure, data-driven budgeting system deployed as both a web application and a cross-platform Flutter mobile application. The system integrates the Plaid API in its Sandbox environment to synchronize simulated banking data for secure testing without exposing live financial credentials. Transaction data is processed and stored using Supabase with a relational PostgreSQL database structured to enforce normalization, referential integrity, …

Enhancing Financial Audit Operations Through Ai Anomaly Detection, Nadya Cousin, Rebekah Garza, Talisa Gomez

Posters - 2026

❖ Financial auditing plays a critical role in ensuring accuracy, regulatory compliance, and fraud detection in financial reporting

❖ Traditional audit approaches rely heavily on sampling and manual review processes, limiting their ability to scale with increasing data complexity

❖ The rapid growth of high-volume, high-velocity financial data (big data) has exposed significant limitations in traditional auditing, including:

• Incomplete data coverage
• Delayed anomaly detection
• Increased risk of material misstatements

❖ These limitations create a need for scalable, automated, and data-driven audit solutions

❖ Artificial Intelligence (AI), particularly anomaly detection models, enables:

•  Full-population testing
•  Real-time pattern recognition
•  Proactive risk identification

Using Ai-Based Predictive Scheduling To Improve Patient Flow And Reduce Wait Times In Healthcare Clinics, Oscar Martinez

Posters - 2026

• Healthcare systems face increasing challenges in patient access and wait times
• Average wait times for specialist care continue to rise, creating:
  • Delays in treatment
  • Reduced patient satisfaction
  • Increased system inefficiencies (Sanford, 2025)
• A major contributor is operational bottlenecks, defined as:
  • Points of congestion that slow or disrupt service flow
• Hospitals typically operate under process layouts, which:
  • Handle diverse patient needs
  • Reduce specialization efficiency
• Contributing factors to bottlenecks:
  • Physician shortages and burnout
  • Administrative burden
  • Inefficient scheduling systems (Moura & Pinho, 2025)
• AI offers potential solutions through:
  • Predictive scheduling
  • Automation of administrative processes
  • Data-driven optimization of patient flow

Optimizing Retail Grocery Inventory Using Ai And Large Language Models: Evidence On Forecast Accuracy, Waste Reduction, And Cost Efficiency, Robert Miller, Stephen Garcia, Brandon Ermis

Posters - 2026

Aim: To evaluate how AI and LLMs improve forecasting accuracy, reduce waste, and enhance inventory decision-making

Application Of Open-Source Small Large Language Models For Finance Report Analysis, Tue Vu, Mark Austin, Marcel Tuijn

SMU Data Science Review

The rapid integration of generative AI in finance introduces both opportunities and challenges, particularly when analyzing sensitive data such as Securities and Exchange Commission (SEC) filings. This study investigates the use of open-source Small Large Language Models (SLLMs), deployed locally through the Ollama and LangChain frameworks, combined with Retrieval-Augmented Generation (RAG) for extracting financial insights relevant to index performance and reporting quality. Two key objectives guide this work: (1) benchmarking multiple open-source SLLMs for sentiment analysis, multiple-choice reasoning, and financial question answering, and (2) assessing the feasibility of locally deployed SLLMs for domain-specific financial queries. A standardized set of 50 …

Entropic Foundation Of Finance And Physics: Securities Price Dynamics And Quantum Theory, Mohammad Abedi

Electronic Theses & Dissertations (2024 - present)

In many scientific and financial contexts, we must reason and make predictions under conditions of incomplete information. This dissertation develops Entropic Dynamics (ED) as a unified framework for deriving dynamical laws directly from principles of inference. Within this approach, probability distributions represent states of knowledge, and their evolution is determined through entropy maximization subject to relevant constraints. This leads to a novel concept of entropic time and a formulation of dynamics as an inferential process. In this talk, I will present how ED provides a common foundation across multiple domains. In physics, quantum dynamics for particles and scalar fields in …

Lost In The Language: Data Breaches And The Strategic Fog Of Risk Disclosures, Ling Tuo, Shipeng Han

Accounting Faculty Publications

This study examines whether firms strategically adjust the readability of Item 1A (“Risk Factors”) disclosures following data breaches. Using U.S. firm-year observations from 2006 to 2023, we find that data breaches are associated with a significant decline in Item 1A readability. This decline is not accompanied by a meaningful increase in informational content; instead, post-breach disclosures exhibit higher syntactic complexity, more positive tone, and lower textual similarity to prior and industry peers' filings, consistent with strategic obfuscation rather than transparent reporting. The readability decline is amplified among firms facing higher litigation risk but attenuated among firms with stronger reputations for …

Beyond Words: A Systematic Multimodal Framework For Text, Images, And Extreme Helpfulness In Online Reviews, Alvaro J. Aguado Marin

Dissertations

Online product reviews have become increasingly multimodal, combining text with media-rich elements such as images. However, academic research has largely examined textual features in isolation, overlooking how visual content and its interaction with text shape perceived helpfulness. This dissertation addresses that gap by developing and empirically validating a comprehensive framework capturing how textual, visual, and contextual features collectively influence review evaluation. Grounded in the Elaboration Likelihood Model (ELM) and extended through the Text-Image Elaboration Likelihood Model (TI-ELM), the framework advances understanding of how consumers process content from both user- and business-generated sources. It also lays the foundation for examining emerging …

Modeling Private Debt Using U.S. Consumer Expenditure Data, Stsiapan Dziamentsyeu

Honors Capstones

This project models private household debt among U.S. consumers using data from the Consumer Expenditure Survey (CES) between 2013 and 2023. The analysis focuses on identifying how demographic and economic characteristics, such as income, housing expenditures, education, and occupation, relate to non-mortgage “other” loan balances. After initial model development produced poor residual behavior due to zero-inflation from imputed debt values, the analysis was refined to include only households reporting verifiable debt. Multiple modeling techniques, including AIC-based variable selection and Lasso regularization, were compared under a five-fold cross-validation framework. The Lasso model achieved superior predictive accuracy (RMSE = 1.55, MAE = …

Leveraging Machine Learning And Causal Inference For Loan Default Prediction, Luca Guida

Doctoral Dissertations and Master's Theses

This research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors …

Fun! Friends! Famous People! Why Fans Attend Anime Conventions, Billy Tringali, Maria Alberto, Jeremiah Martinez

Proceedings from the Document Academy

In 2021 during the global Covid-19 lockdowns, Billy and Maria ran an IRB-exempted online survey, looking to hear from fans who attend anime conventions. Conventions had been shut down as non-essential services that drew large crowds, and we hoped to capture a screenshot of this moment, to better learn from it in the future. And the resulting data collection went quite well – we were able to partner with a major anime organization to share the survey, and our 1000+ respondents had a lot to say about the conventions they were missing during lockdowns.

Ultimately, we found a significant emphasis …

Impacts Of Climate Disruption On Mobility Aircraft Performance In The Pacaf Region, Hannah M. Dauterman

Theses and Dissertations

This thesis investigates the projected impacts of climate disruption on the performance and fuel management of the C-17 Globemaster III, a critical mobility aircraft in the Pacific Air Forces (PACAF) region. As rising global temperatures reduce air density, the performance of aircraft is compromised, resulting in increased fuel consumption, as well as the potential for extended runway requirements and diminished cargo capacity. Using climate projection data from Coupled Model Intercomparison Project Phase 6 (CMIP6), this research analyzes future air temperature trends and their implications for C-17 fuel consumption. Results suggest that by 2049, the U.S. Air Force may incur an …

Nba Player Types And Salaries: Assessing The Disparities In Pay, Nick Riccardi, Rodney J. Paul

Sport Management - All Scholarship

The purpose of this study was to identify player types that exist in the modern National Basketball Association (NBA), test whether player types are paid differently controlling for performance and other factors and construct successful rosters with cheaper payrolls.
We collected performance statistics and salary data for players and teams across five seasons (2018-19 to 2022-23). Cluster analysis is leveraged to group together player-seasons to identify the player types that exist in the NBA. Linear regression models are run to test for differences in pay by cluster membership while controlling for performance, age, and contractual details. Linear programming simulation models …

Family Ties: Nba Draft Position And Player Performance, Nick Riccardi, Rodney J. Paul

Sport Management - All Scholarship

This study aims to investigate the role, if any, that nepotism plays in the careers of players in the National Basketball Association (NBA). Career performance is compared between the 780 players drafted from 2007-2019 with familial relationships considered. Ordinary Least Squares and logistic regression models are specified to estimate the effect of having a relative on the success of an NBA player’s career. We find that siblings of NBA players earn more and reach minimum games played thresholds more often, while sons of NBA players earn more, but generally do not reach games played thresholds more often than similarly-drafted peers.

A Cancer Education Needs Assessment: Informing Middle-Aged Female Patients About The Relationships Between Obesity And Women’S Health Concerns In The Reproductive System, Breast, And Endometrial Health, Batul Mirza

MUSC Theses and Dissertations

Obesity significantly impacts women’s health, particularly among middle-aged women, by increasing the risk of hormone-sensitive cancers such as breast, endometrial, and reproductive system cancers. This study examines the educational needs of this demographic group regarding obesity-related cancer risks and explores effective intervention strategies. Obesity-induced mechanisms – hormonal imbalances, chronic inflammation, and insulin resistance – drive cancer susceptibility, emphasizing the need for targeted health education. The study employs a qualitative design, which includes interviews with subject matter experts (SMEs) and surveys of middle-aged women. The goal is to assess awareness, perceived barriers, and preferred learning methods. Findings suggest that with many …

From Disruption To Integration: Cryptocurrency Prices, Financial Fluctuations, And Macroeconomy, Zhengyang Chen

Faculty Publications

This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrency's deep financial system …

Resale Revolution: Trend Implications From Media Presence Transcended To Luxury Retail Markets, Penelope Prochnow

Capstone Projects

This study aims to deepen understanding of fashion trend decline from peak popularity to obsolescence, with implications for sustainability and producer profit margins. It investigates how the attributes and media presence of fashion items influence their journey from high-end editorial coverage to resale platforms. Using survival analysis to model trend lifetimes and cosine similarity metrics to compare resale and magazine keyword frequencies, alongside machine learning for price prediction, the study uncovers critical temporal patterns. Results show that resale trends reflect magazine content with a lag of approximately 18 to 30 months and draw from long-wave revivals spanning 6 to 14 …