Data Science Commons^™

Unpacking The Orders, Leonard J. Santos

Dissertations, Theses, and Capstone Projects

At the end of the first week of his presidency this year in 2025, Donald Trump signed a total of thirty-six executive orders. After only one month, that number had gone up to seventy-six. He is currently on track to sign the highest number of executive orders in his first year in office of any president in the last 50 years, and potentially even the last 100 years if he keeps moving at this rate. In the first 90 days of his term, Donald Trump has signed 159 executive orders, more than any other president in the history of the …

Transforming The Future Of Health: Building Learning Health Systems Across The Globe, Sandra Yankah, Robert Saunders, Mark L. Tykocinski, Claudia Salzberg, Jonathan Gonzalez-Smith, Rachel Bonesteel, Cameron Joyce, Charles Kahn, Mark Mcclellan, Eyal Zimlichman

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Health care has faced disruptions over the past 5 years, including a global pandemic, supply chain interruptions, workforce shifts, and the introduction of new artificial intelligence (AI) tools. Health care organizations continue to leverage the learning health system (LHS) concept to adapt to these challenges through iterative feedback loops. The Future of Health (FOH), an international community of over 50 senior health leaders that focuses on shared challenges across international health systems, collaborated with the Duke-Margolis Institute for Health Policy in a consensus-building process with FOH members to identify opportunities for action in an LHS. Key areas for action identified …

Data Driven Analysis Of Samara Seed Kinematics And Dynamics, Shashwat Sparsh

Master's Theses

Samara Seeds are a class of fruit most famously belonging to the Acer species and are characterized by their single-bladed geometry and their auto-rotation response during descent. This steady-state auto-rotation response is the subject of aerodynamic analysis which aim to quantify the performance. The period prior to the beginning of steady-state auto-rotation is classified as the transition regime and has not been the subject of intense scrutiny.

This thesis employs a data-driven approach to analyzing the kinematic and dynamic response of these seeds during both the transition and auto-rotation stages of flight to quantify the performance with respect to the …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Towards Explainable Ai On Graph Neural Networks: Xaig, Jiaxing Zhang

Dissertations

In the evolving landscape of artificial intelligence (AI), Graph Neural Networks (GNNs) have garnered growing prominence for their adeptness in processing graph-structured data. Despite this, the interpretability of their predictions often remains elusive. The demand for transparency and explainability in complex prediction models has reached unprecedented levels. To address this, post-hoc instance-level explanation techniques have emerged, aiming to unveil the rationale behind GNN predictions. These techniques endeavor to unearth substructures that elucidate the predictive behavior of trained GNNs.

This dissertation embarks on an exploration of Explainable AI (XAI) technologies within the realm of GNNs. Amid the challenges posed by the …

A Novel Framework For Dynamic Graph Representation Learning With Mamba, Ashish Pandey

Theses

Dynamic graph embedding is a key technique for modeling temporal dependencies in evolving networks. While transformer-based models perform well, their quadratic complexity limits scalability on long graph sequences. This thesis compares transformer approaches with the Mamba architecture-a linear-complexity state-space model—for temporal graph embedding.

Two frameworks are proposed: DG-Mamba and GDG-Mamba. DG-Mamba uses standard GCN-based spatial encoding, while GDG-Mamba incorporates domain-aware edge features using Graph Isomorphism Network with Edge Convolution (GraphGINE). Experiments on UCI, Reality Mining, Slashdot, Bitcoin-OTC, and SBM datasets show that Mamba-based models match or exceed transformer performance, especially on graphs with high temporal variability.

The thesis also applies …

Surface Characterization Of Asian Lacquers Using Surface Metrology And Data Science: Introducing The Roughness Spectrum, Ravines Patrick, H. David Sheets, Marianne Webb, Joy Mazurek, Michael R. Schilling, Herant Khanjian

Computer and Data Science Faculty Publications

No abstract provided.

Protein Word Detection Using Text Segmentation Techniques, Ashish V. Tendulkar, Sutanu Chakraborti, Ganesh Devi

Journal of Global Awareness

Literature in Molecular Biology is abundant with linguistic metaphors. There has been works in the past that attempt to draw parallels between linguistics and biology, driven by the fundamental premise that proteins have a language of their own. Since word detection is crucial to the decipherment of any unknown language, we attempt to establish a problem mapping from natural language text to protein sequences at the level of words. Towards this end, we explore the use of an unsupervised text segmentation algorithm for the task of extracting "biological words” from protein sequences. We demonstrate the effectiveness of using domain knowledge …

Mat 301 - Applied Statistics And Data Analysis, Eric Aragundi

Open Educational Resources

Data analysis using standard statistical methods and relevant computer software. Emphasis on real-world data, interpretation, and misinterpretation of computer output.

This syllabus contains open source notebook about data analysis content.

Dynamate: Leveraging Ai-Agents For Customized Research Workflows, Orlando A. Mendible-Barreto, Misael Díaz-Maldonado, Fernando J. Carmona Esteva, J. Emmanuel Torres, Ubaldo M. Córdova-Figueroa, Yamil J. Colón

Computer and Data Science Faculty Publications

No abstract provided.

A Complete Transfer Learning-Based Pipeline For Discriminating Between Select Pathogenic Yeasts From Microscopy Photographs, Ryan A. Parker, Danielle S. Hannagan, Jan H. Strydom, Christopher J. Boon, Jessica Fussell, Chelbie A. Mitchell, Katie L. Moerschel, Aura G. Valter-Franco, Christopher Cornelison

Faculty Articles

Pathogenic yeasts are an increasing concern in healthcare, with species like Candida auris often displaying drug resistance and causing high mortality in immunocompromised patients. The need for rapid and accessible diagnostic methods for accurate yeast identification is critical, especially in resource-limited settings. This study presents a convolutional neural network (CNN)-based approach for classifying pathogenic yeast species from microscopy images. Using transfer learning, we trained the model to identify six yeast species from simple micrographs, achieving high classification accuracy (93.91% at the patch level, 99.09% at the whole image level) and low misclassification rates across species, with the best performing model. …

Historical Perspectives In Volatility Forecasting Methods With Machine Learning, Zhiang Qiu, Clemens Kownatzki, Fabien Scalzo, Eun Sang Cha

All Faculty Open Access Publications

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to …

Histone Methyltransferase Ash1l Primes Metastases And Metabolic Reprogramming Of Macrophages In The Bone Niche, Chenling Meng, Kevin Lin, Wei Shi, Hongqi Teng, Xinhai Wan, Anna Debruine, Yin Wang, Xin Liang, Javier Leo, Feiyu Chen, Qianlin Gu, Jie Zhang, Vivien Van, Kiersten L Maldonado, Boyi Gan, Li Ma, Yue Lu, Di Zhao

Faculty, Staff and Student Publications

Bone metastasis is a major cause of cancer death; however, the epigenetic determinants driving this process remain elusive. Here, we report that histone methyltransferase ASH1L is genetically amplified and is required for bone metastasis in men with prostate cancer. ASH1L rewires histone methylations and cooperates with HIF-1α to induce pro-metastatic transcriptome in invading cancer cells, resulting in monocyte differentiation into lipid-associated macrophage (LA-TAM) and enhancing their pro-tumoral phenotype in the metastatic bone niche. We identified IGF-2 as a direct target of ASH1L/HIF-1α and mediates LA-TAMs' differentiation and phenotypic changes by reprogramming oxidative phosphorylation. Pharmacologic inhibition of the ASH1L-HIF-1α-macrophages axis elicits …

Challenging $\Lambda$Cdm: Unraveling Cosmic Distances, Dark Sector Phenomenology, And Alternative Primordial B-Mode Sources, Kylar L. Greene

Physics & Astronomy ETDs

The dominant Lambda Cold Dark Matter (LCDM) cosmological model, while remarkably successful, increasingly shows signs that it may not fully describe our Universe, as persistent tensions in expansion rates and structure formation remain unresolved. In this thesis, I challenge the LCDM paradigm using novel theoretical frameworks combined with rigorous numerical analyses. I demonstrate that the expansion-rate tension fundamentally reflects underlying distance disagreements, and that the Thomson scattering rate strongly restricts higher pre-recombination expansion rates without additional physics. Further, I present a novel cosmological model using a mirror dark sector and varying fundamental constants, revealing an observational degeneracy allowing significantly higher …

Data Encoding, Compilation, And Algorithms For Quantum Machine Learning, Aviraj Sinha

Computer Science and Engineering Theses and Dissertations

Quantum computing enables new approaches to data processing, especially in quantum machine learning. Unlike classical systems, quantum data must be synthesized through operations and can exist in superposition. Encoding choices affect efficiency, noise resilience, and trainability—key factors in quantum machine learning models. This dissertation enhances quantum data encodings by extending quantum read-only memory (QROM) beyond binary representations, improving efficiency and parallelism. It introduces new compilation methods for quantum random number generators (QRNGs), supporting non-parametric distributions for post-quantum cryptography. Additionally, it explores Cayley graph-based encodings to extract spectral features for quantum machine learning.

Hybrid Graph-Recurrent Architecture For Citation Recommendation Via Future Embedding Forecasting, Mohammad Ausaf Ali Haqqani

Computer Science and Engineering Theses and Dissertations

The rapid expansion of scientific literature has intensified the challenge of identifying relevant citations, particularly for newly published or under-cited papers. Traditional citation recommendation systems typically model static relationships or respond to past citation activity, offering limited predictive power for emerging works. In response, this thesis presents a temporal modeling framework for citation recommendation that anticipates future scholarly relevance by forecasting the latent representations of academic papers.

Building on prior work that utilized Temporal Graph Networks (TGNs) to model dynamic citation flows, we propose Graph-Time, a hybrid architecture that integrates a Graph Transformer with a GRU-based time series predictor. The …

A Computational Method For Detecting Compound Promiscuity In Early-Stage Pharmaceutical Discovery, John Allen Ringer

Computer Science ETDs

Modern drug discovery and chemical biology research relies heavily on analyzing bioassay data. One of the many challenges in bioassay data analysis is identifying false trails, i.e., chemical compounds which initially appear to have desirable activity but are found to be problematic upon further investigation. Badapple (the BioAssay-Data Associative Promiscuity Pattern Learning Engine) was created over ten years ago to help researchers identify promiscuous compounds and thus avoid a common source of these false trails. Through an effort involving software engineering, cheminformatics, and biomedical data science we have developed Badapple 2.0, which incorporates updated assay records and expanded data semantics. …

Incorporating Latent Survival Trajectories And Covariate Heterogeneity In Time-To-Event Data Analysis: A Joint Mixture Model Approach, Fu-Wen Liang, Wenyaw Chan, Michael D Swartz, Bouthaina S Dabaja

Faculty, Staff and Student Publications

Background: Finite mixture models have been recently applied in time-to-event data to identify subgroups with distinct hazard functions, yet they often assume differing covariate effects on failure times across latent classes but homogeneous covariate distributions. This study aimed to develop a method for analyzing time-to-event data while accounting for unobserved heterogeneity within a mixture modeling framework.

Methods: A joint model was developed to incorporate latent survival trajectories and observed information for the joint analysis of time-to-event outcomes, correlated discrete and continuous covariates, and a latent class variable. It assumed covariate effects on survival times and covariate distributions vary across latent …

Gnns For Network Classification In Single Cell Rna Sequencing Data, Reid C. Sewell

Capstone Projects

A common technique when investigating a disease is to profile gene expression, as this gives unique insights into the functions of a cell. Gene expression data gathered from single cell RNA sequencing can be encoded into a gene co-expression network, which is a graph of potential relationships between different genes. One method for interpreting data encoded as a graph is to use a graph neural network, or GNN. This project designs and implements a GNN architecture to accomplish classification tasks on graph data. Then, given a dataset of gene co-expression networks made from multiple single cell RNA sequencing studies, the …

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 …