Research Projects
The Data and Decision Sciences Lab is also engaged in producing impactful research to advance the field and build the foundations of data science, operations research, and statistics.
Our team together has published more than 100 pier-reviewed papers in top-tier scientific journals and prestigious conference proceedings. Our work has been cited by hundreds of other researchers in the field and has been used to support and advance knowledge in data science, operations research, and statistics. Some of the research developed by our team include:
Applied Research
Predicting Nursing Baccalaureate Program Graduates using Machine Learning Models: A Quantitative Research Study
Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics
Simple Multi-Attribute Rating Technique for Renewable Energy Deployment Decisions (SMART REDD)
SPOT Panchromatic Imagery and Neural Networks for Information Extraction in a Complex Mountain Environment
Foundations of Data Science
Measuring Lineup Difficulty By Matching Distance Metrics With Subject Choices in Crowd-Sourced Data
Using Visual Statistical Inference to Better Understand Random Class Separations in High Dimension, Low Sample Size Data
Optimization Algorithms
A Two Dimensional Search Primal Affine Scaling Interior Point Algorithm for Linear Programs
by Fabio Vitor
A Subtree-Partitioning Algorithm for Inducing Parallelism in Network Simplex Dual Updates
Parallel Simplex for Large Pure Network Problems: Computational Testing and Sources of Speedup
Theoretical Probability and Statistics
Necessary Sample Sizes for Specified Closeness and Confidence of Matched Data under the Skew Normal Setting
by Cong Wang
The Harmful Effect of Null Hypothesis Significance Testing on Marketing Research: An Example
by Cong Wang
Extending the a Priori Procedure to One-way Analysis of Variance Model with Skew Normal Random Effects
by Cong Wang
Some New Bounds and Approximations on Tail Probabilities of the Poisson and Other Discrete Distributions
by Steven G. From and Andrew W. Swift
A Comparison of the Moment and Factorial Moment Bounds for Discrete Random Variables
A Weighted Least-Squares Procedure for Estimating the Parameters of Altham’s Multiplicative Generalization of the Binomial Distribution
An Application of Cheng’s Lemma for Minimizing the Asymptotic Variance of Best Asymptotically Normal Estimators Based on Sample Quantiles
Educational Research
Student Learning and Perceptions in a Flipped Linear Algebra Course
by Betty N. Love and Andrew W. Swift
