Data Science Commons^™

Ai-Powered Compliance: Accelerating Efficiency And Decision-Making For Compliance Related Inquiries., Amberly R. Rodriguez

SMU Data Science Review

This research examines the potential of an AI-powered chatbot to streamline compliance workflows by reducing the time and effort required to locate and interpret complex compliance documents. The prototype integrates a centralized MySQL-based document repository, a contextual document querying engine, and a Streamlit web interface, enabling employees to retrieve accurate, document-backed answers within seconds. The system supports both stored and user-uploaded documents, with features such as automated summarization and source citations to enhance transparency and trust. Manual evaluation demonstrated notable gains in efficiency and accuracy compared to traditional search methods, with strong potential to improve adherence to compliance policies. Future …

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

A Comparative Time Series Analysis Of The Arima And Temporal Fusion Transformer (Tft) Models, Catherine Ticzon, Aaron Abromowitz, Bivin Sadler

SMU Data Science Review

Several new transformer-based time series models have been developed in the past five years and research has provided evidence of these models’ superior performance compared to classic statistical models such as ARIMA. While transformer-based models show impressive performance on baseline datasets, no research has been done on the robustness of these models on datasets with controlled modifications and in a replicable manner. In this paper, the Temporal Fusion Transformer (TFT) model was compared to the classical statistical model ARIMA on simulated data using multiple horizons. Data were simulated using a linear combination of exogenous variables; in total, 50 realizations of …

Analyzing The Global Happiness Index, Victoria Hernandez, Christy W. Wachira

SMU Data Science Review

This study explores the Global Happiness Index using data compiled from the OECD and Our World in Data to identify key factors contributing to societal well-being. Six primary predictors were analyzed: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption. Regression and clustering techniques were employed to uncover patterns among countries. By expanding the analytical scope beyond conventional economic and social indicators, this study helps identify new pathways for improving well-being across diverse cultural and economic landscapes. Additional variables such as perceived safety, political engagement, and values related to family and …

A Blockchain-Enabled Deep Learning Framework For Secure Omics Data Sharing And Attack Detection, Don Roosan, Md Rahatul Ashakin, Rubyat Kahn, Mazharul Karim

Computer and Data Science Faculty Publications

No abstract provided.

Shape: Spatial Health And Population Estimator, Emma M. Von Hoene, Aanya Gupta, Hamdi Kavak, Amira Roess, Taylor Anderson

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Explainable Post-Operative Patients Recovery Prediction Following Elective Brain Tumor Resection: A Precision Medicine Approach, Eleanor Belkin

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling The Cancer Cell Growth Predictions Based On Classical Mathematical Models With Physics-Informed Neural Network, Widodo Samyono

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Reconstructing Gene Regulatory Networks From Time-Series Data In Knime, Raina Robeva

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

[Project Insight] [Foag] Data-Driven Machine Learning Approaches To Modeling Pertussis Vaccine Scare Behavior, Gleb Gribovskii

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling Social Isolation: A Data-Driven Index Function Design And Implementation, Jeremis N. Morales Morales, Carmen Caiseda, Phyllis Muniu, Joshua Atsu, Folashade B. Agusto

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

[Project Insight] Modeling Social Isolation: A Data-Driven Index Function Design And Implementation, Jeremis Morales-Morales

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Machine Learning–Based Prediction Of Bleeding Risk In Factor Xi Deficiency, Tracey G. Oellerich, Stephanie Reitsma, Alisa Wolberg, Karin Leiderman, Suzanne Sindi

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Correlation With Car Density In Urban Environments And Its Influence On Chronic Obstructive Pulmonary Disease (Copd) Rates In The United States, Daniel Barreiro-Torres, Kedai Cheng

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Interpretable Machine Learning For Cardiovascular Risk Prediction: Insights From Nhanes Dietary And Health Data, Md Ahiduzzaman, Md Nahid Hasan

Faculty Publications

Background: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination Survey (NHANES) data from 2017–2023 to predict CVD risk based on dietary and health factors.

Methods: We analyzed data from 12,382 adults (aged 18 and older) from NHANES 2017–2023, including 41 dietary, anthropometric, clinical, and demographic variables. Recursive Feature Elimination (RFE) was used to select an optimal subset of 30 predictors. To address substantial class imbalance in the outcome, we applied the Random Over-Sampling Examples …

Computational Data Analysis, Kathryn S. Biles

LSU Master's Theses

Data science has emerged as a cornerstone of innovation, shaping an ever-expanding range

of professional careers. As technology advances and the volume of data expands expo-

nentially, the ability to extract meaningful insights from data has become indispensable

across industries. Far from representing a single career path, data science enables profes-

sionals in nearly every domain to make informed decisions, optimize systems, and drive

innovation. Yet, many high school students and incoming college freshmen have limited

exposure to data science fundamentals or the career opportunities they unlock. This is

the gap that Computational Data Analysis, a high school-level curriculum I …

A Hybrid Data Assimilation Approach For Parameter Estimation In Dynamical Systems, Xuejian Li

Math Department Colloquium Series

In this talk, we present a hybrid data assimilation (DA) method that integrates continuous data assimilation (CDA) with particle filtering to estimate parameters in dynamical systems. Parameter estimation in such systems is particularly challenging because it involves both determining the parameters and estimating the often high-dimensional physical state. To address this difficulty, we decouple the estimation of states and parameters by employing CDA for state estimation and particle filtering for parameter estimation, with information exchanged alternately between the two. This hybrid framework leverages the strengths of CDA in handling high-dimensional state estimation and the efficiency of particle filters in estimating …

Validation Of A Risk Score For Cancer-Associated Thrombosis Using Nationwide Ehr Data, Ang Li, Omid Jafari, Barbara D Lam, Jun Y Jiang, Rock Bum Kim, Shengling Ma, Emily Zhou, Joyce W Tiong, Elizabeth C Chiang, Justine Ryu, Christopher I Amos, Jennifer La, Nathanael R Fillmore

Faculty, Staff and Student Publications

Importance: Venous thromboembolism (VTE) is associated with increased mortality and morbidity in patients with cancer. Existing risk prediction models are typically validated within individual sites, a fragmented approach that limits clinical adoption.

Objective: To validate the electronic health record cancer-associated thrombosis (EHR-CAT) score compared with the benchmark Khorana score in a contemporary cohort of patients with cancer across the nation, before and after treatment, excluding those at high risk of bleeding.

Design, setting, and participants: This prognostic study included patients in a nationwide longitudinal EHR database from January 2018 to December 2023 with follow-up continuing to April 2025. Patients with …

Spatio-Temporal Gcn With Softmax Classifier For Skeleton-Based Human Action Recognition, Kabul Khudaybergenov, Avazjon Marakhimov, Zahriddin Muminov

Chemical Technology, Control and Management

Skeleton-based human action recognition is an important research area with many practical applications. Most existing methods rely on single representations of skeletal sequences, which cannot totally obtain all the complex features of human movements. This paper presents LFHAR (Latent Features for Human Action Recognition), a new framework that uses multiple spatio-temporal latent representations to improve the extraction of action features. Our method captures how skeletal poses change over time and combines motion information from both individual joints and connected body parts. The proposed approach applies graph-based processing to each skeleton frame in a sequence, then arranges the resulting graph features …

Atlas Of Ai: Power, Politics And The Planetary Costs Of Artificial Intelligence - Book Review, Jelena Popov

Feminist Pedagogy

No abstract provided.

A Review Of Research And Practices On Teaching Data Visualizations For Blind And Visually Impaired Students, Shiya Cao

Statistical and Data Sciences: Faculty Publications

Around 36 million people in the world are blind and an additional 217 million have moderate to severe vision impairment. In higher education, four percent of 54,204 undergraduates who participated in the 2022 American College Health Association survey reported to be blind or have low vision. Those students frequently do not have access to data visualizations we generally teach and use in postsecondary statistics and data science classes. The design of those visualizations is premised on implicit assumptions about the user’s visual ability. Making data visualizations accessible to blind and visually impaired (BVI) people would help improve equity in higher …

Smarter Disease Detection From Electronic Health Record Data: An End-To-End Ai-Augmented Pipeline For Computable Phenotyping, Dylan Owens

Statistical Science Theses and Dissertations

Electronic Health Records (EHR) contain a wealth of structured and unstructured patient data that can be leveraged for computable phenotyping, the process of algorithmically identifying patient cohorts with specific diseases or conditions. Traditional rule-based phenotyping approaches, while interpretable, often struggle with scalability, portability across institutions, and effective use of unstructured clinical narratives. Recent advances in large language models (LLMs) present new opportunities for synthesizing complex free-text information into concise, clinically meaningful representations. However, integrating LLMs into phenotyping workflows requires careful design to maintain transparency, interpretability, and measurable uncertainty—features essential for clinical adoption and downstream applications such as decision support.

We …

Building An Inclusive Ai Chatbot For Diverse Student Communities At Cal Poly: Uplift Ai, Gideon Telahun

College of Engineering Summer Undergraduate Research Program

This research project will investigate the ability of advanced Large Language Models (LLMs) to identify and assess misinformation across diverse forms of media, including text, images, and video. In an age where misleading content spreads rapidly across digital platforms, evaluating the reliability and integrity of AI systems tasked with fact-checking is critical. We will develop a comprehensive dataset composed of factual and misleading examples drawn from various well-known and reliable fact-checking organizations. Each item will be independently reviewed and transparently labeled to ensure reproducibility. We will then prompt a curated group of state-of-the-art LLMs—including GPT-4, Claude, Gemini, Perplexity, Grok, and …

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 …

The Science Of Sound: Studying The Cognitive, Emotional, And Physiological Effects Of Frequency, Genre, And Music, Jett Yarborough

Senior Honors Theses

Music influences emotion, physiology, and cognition, yet little is known about how the frequency it is tuned to affects these influences. Prior research has shown that music tuned to 432 Hz can reduce stress, lower blood pressure, and improve sleep. My colleagues and I conducted two studies to investigate this. Our research found that music tuned to 432 Hz promotes a significant increase in memory retention and induces a state of focus and relaxation. These results suggest that shifting from the standard tuning frequency of 440 Hz to 432 Hz could have a profoundly positive impact on our daily lives. …

Heuristic Weight Initialization For Transfer Learning In Classification Problems, Musulmon Lolaev, Anand Paul, Jeonghong Kim

School of Public Health Faculty Publications

Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models. Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully. Most existing initialization methods mainly focus on gradient flow-related problems, such as gradient vanishing or exploding, or other existing approaches that require extra models that do not consider our setting, which is more practical. To address these problems, we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully …

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 …

Greedy Algorithm For Neural Networks For Indefinite Elliptic Problems, Qingguo Hong, Jiwei Jia, Young Ju Lee, Ziqian Li

Mathematics and Statistics Faculty Research & Creative Works

The paper presents a priori error analysis of the shallow neural network approximation to the solution to the indefinite elliptic equation and a cutting-edge implementation of the Orthogonal Greedy Algorithm (OGA) tailored to overcome the challenges of indefinite elliptic problems, which is a domain where conventional approaches often struggle due to the lack of coerciveness. A rigorous priori error analysis that shows the neural network's ability to approximate the solution of indefinite problems is confirmed numerically by OGA. We also present the error analysis of the relevant numerical quadrature. In particular, massive numerical implementations are conducted to justify the theory, …

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Scientific Multimodal Summarization : Integrating Knowledge Across Textual, Visual And Auditory Content, Zusheng Tan

Lingnan Theses (MPhil & PhD)

As scientific publications increasingly incorporate multimodal content, ranging from textual descriptions to figures, tables, presentation videos, and audio, there is a growing need for summarization systems that can effectively process and integrate information across these diverse modalities.

This work presents a comprehensive exploration of Scientific Multimodal Summarization, introducing a series of novel architectures and datasets aimed at advancing this emerging field. 1): We begin by introducing CMT-Sum, which integrates multimodal scientific source content (i.e., primarily paper text and figures) to generate high-quality textual summaries and identify representative graphical abstracts. We refer to this task as Scientific Multimodal Summarization with …