The Ph.D. in Analytics and Data Science is an advanced degree with a dual focus of application and research - where students will engage in real world business problems, which will inform and guide their research interests.

To ensure that our Ph.D. students in Analytics and Data Science are exposed to the latest issues and challenges of working across a wide variety of data contexts, individuals will be required to engage with one (or more) of the dozens of organizations which have agreed to sponsor doctorate-level projects for a minimum of three semesters (9 credit hours of engagement + 15 credit hours of dissertation research). These organizations span the continuum of application domains, including health care, banking, retail, government, and consumer finance. Students will also continue to work with the faculty adviser through their final year of project engagement and dissertation research.

The materials in this collection consists of research conducted by PhD candidates as a means to showcase the important work being done in the program.

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Submissions from 2023

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A Multistage Framework for Detection of Very Small Objects, Duleep Rathgamage Don, Ramazan Aygun, and Mahmut Karakaya

Submissions from 2022

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Applications of Integrated Gradients in Credit Risk Modeling, Md Shafiul Alam, Jonathan Boardman, Xiao Huang, and Matthew Turner

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A New Kind of Data Science: The Need for Ethical Analytics, Jonathan Boardman

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Integrated Gradients is a Nonlinear Generalization of the Industry Standard Approach to Variable Attribution for Credit Risk Models, Jonathan Boardman, Md Shafiul Alam, Xiao Huang, and Ying Xie

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ExplainabilityAudit: An Automated Evaluation of Local Explainability in Rooftop Image Classification, Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, and Girish Modgil

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Directional Pairwise Class Confusion Bias and Its Mitigation, Sudhashree Sayenju, Ramazan Aygun PhD, Jonathan Boardman, Duleep Prasanna Rathgamage Don, Yifan Zhang PhD, Bill Franks, Sereres Johnston PhD, George Lee, Dan Sullivan, and Girish Modgil PhD

Submissions from 2019

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Radically Simplifying Gated Recurrent Architectures Without Loss of Performance, Jonathan Boardman and Ying Xie

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Evaluating the Impact of Proactive Care Management with IDStrat, D.J. Donahue and Lauren Staples

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Outcome Prediction in Intensive Care Unit Settings with Claims Data, Lauren Staples and Ryan Rimby

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A Product Affinity Segmentation Framework, Lili Zhang, Jennifer Priestley, Joseph DeMaio, and Sherry Ni

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A Descriptive Study of Variable Discretization and Cost-Sensitive Logistic Regression on Imbalanced Credit Data, Lili Zhang, Jennifer Priestley, Herman Ray, and Soon Tan