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

Nebraska Education Governance and Spending Briefs

Melanism as a Potential Thermal Benefit in Eastern Fox Squirrels

Warnings about Simulation Revisited: Improving Operations in Congonhas Airport

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)

Proof-of-Concept for a Green Energy Linear Program for Optimizing Deployments

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

Validation of Visual Statistical Inference, Applied to Linear Models

Optimization Algorithms

Approximate and Exact Merging of Knapsack Constraints with Cover Inequalities

The Double Pivot Simplex Method

The Ratio Algorithm to Solve the Optimal Basis of Two Constraint Linear Programs

A Two Dimensional Search Primal Affine Scaling Interior Point Algorithm for Linear Programs

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

Some New Upper and Lower Bounds for the Mills Ratio

Extending the a Priori Procedure to One-way Analysis of Variance Model with Skew Normal Random Effects

Some New Bounds and Approximations on Tail Probabilities of the Poisson and Other Discrete Distributions

Extending A Priori Procedure to Two Independent Samples under Skew Normal Settings

by  Cong Wang

From a Sampling Precision Perspective, Skewness Is a Friend and Not an Enemy!

by  Cong Wang

A Flexible Distribution Class for Count Data

A Comparison of the Moment and Factorial Moment Bounds for Discrete Random Variables

Stochastic Models of Cascading Failures

A Weighted Least-Squares Procedure for Estimating the Parameters of Altham’s Multiplicative Generalization of the Binomial Distribution

Confidence Intervals for Expected Coverage from a Beta Testability Model

An Application of Cheng’s Lemma for Minimizing the Asymptotic Variance of Best Asymptotically Normal Estimators Based on Sample Quantiles

Educational Research

Collaborative Big Data Review for Educational Impact

Student Learning and Perceptions in a Flipped Linear Algebra Course