Exploring Research Contributions at UCF: A Focus on Data Science and Interdisciplinary Studies

This article examines recent research initiatives and faculty contributions at the University of Central Florida (UCF), with a particular emphasis on data science, statistics, and interdisciplinary studies. By highlighting specific projects and faculty expertise, this article aims to provide insights into the diverse research landscape at UCF and its impact on various fields.

The Unification of Quantitative Sciences at UCF

Florencio “Eloy” Hernandez serves as Director, guiding the transition to merge the Department of Mathematics and the Department of Statistics and Data Science into a single school. This marks a significant step forward in unifying and advancing UCF’s quantitative sciences. This integration promises to foster greater collaboration and innovation in these interconnected fields.

Faculty Spotlight: Expertise in Statistics and Data Science

Several faculty members at UCF are making significant contributions to the fields of statistics and data science.

Suyeon Kang: An Assistant Professor in Statistics and Data Science at the University of Central Florida. Her primary research interests include multivariate analysis, sensitivity analysis, causal inference, and mixture models, with applications in disparities research, social sciences, and health sciences. Previously, she was a postdoctoral researcher at the University of Florida.
Jialin Liu: An Assistant Professor in Statistics and Data Science at the University of Central Florida (UCF) and a member of the AI Initiative at UCF. He earned his B.S. degree in Automation from Tsinghua University in 2015 and his Ph.D. in Applied Mathematics from the University of California, Los Angeles (UCLA) in 2020.
Dr. HanQin Cai: Currently the Paul N. Somerville Endowed Assistant Professor for Statistics, at UCF Statistics and Data Science. He is also the director of Data Science Lab. He received his Ph.D. degree in Applied Mathematics and Computational Sciences from University of Iowa, where he also received two Master’s degrees in Computer Science and Mathematics respectively.
Larry Tang: A statistician specializing in statistical methodology and collaborative research. His current methodological research areas include statistical methods in forensics, diagnostic medicine, group sequential designs and substance abuse research and criminology. He received his Ph.D. in Statistics from Southern Methodist University in 2005.
Rong Zhou: Received her M.S. in Statistics from South Dakota State University and wrote her thesis on comparison of software packages for detecting differentially expressed genes from single-sample RNA-seq data.

Student Research Showcases: High-Impact Practices

UCF provides opportunities for students to engage in high-impact research practices, allowing them to apply their knowledge and contribute to various fields. Several student projects presented at the High Impact Practices Student Showcase exemplify this.

The Weight of Waking: Postpartum Mental Health and Infant Sleep

Casey Courtright, a clinical psychology major with a minor in statistics, presented a study titled "The Weight of Waking: The Relationship Between Postpartum Mental Health and Infant Sleep." This study explored which factors act alongside maternal mental health to predict how often their infants awoke at night. More specifically, this project aimed to determine if education level interacted with postpartum mental health symptoms to predict awakenings.

A multiple linear regression model was developed using a dataset on 410 new mothers from Switzerland. The data contained information on the mothers’ recent symptoms of several mental health conditions as well as their infants’ sleep quality. Despite finding a statistically significant model, it did not account for much of the pattern in infant sleep awakenings. Future research should include mothers from countries with more pronounced socioeconomic inequality to gain a more diverse perspective on the effects of socioeconomic status on maternal mental health and infant sleep.

The research team extended their sincere gratitude to Professor Nathaniel Simone for his enthusiasm and constant support. The guidance and teaching opportunities he provided played a pivotal role in the troubleshooting process.

The Impact of Gun Violence: A Study on Mass Killings in the United States

Abigail Wright, Oma Persaud, and Tindrew Chen presented a study titled "The Impact of Gun Violence: A Study on Mass Killings in the United States." This study examines the impacts of gun violence in the United States from 2006 to October 2024, focusing on factors influencing the casualties in mass shooting incidents.

Utilizing the predictors: number of offenders, region, location, incident type, and gun type they employed a Negative Binomial Regression Model due to its suitability for handling high variance and over-dispersion in the response variable. The dataset "Mass Killings in America" reports 1,018 casualties from over 100 incidents in the past four years alone.

The research team also expressed their deep appreciation to The Associated Press, USA Today, and Northeastern University for their commitment to maintaining and updating the Mass Killings Dataset.

Night Code: Decoding Nighttime Health Through Data

Alexie Fischer and Rishitavani Donapati presented a project titled "Night Code: Decoding Nighttime Health Through Data." This project explores how variables such as stress levels, BMI, physical activity, blood pressure, and occupation impact sleep patterns using the Sleep Health and Lifestyle Dataset from Kaggle.

The project reveals that stress levels negatively affect both sleep duration and quality, while physical activity shows a positive correlation with improved sleep outcomes. Additionally, BMI and blood pressure demonstrate moderate associations with sleep, emphasizing the role of cardiovascular health in sleep patterns. The research also explores how different occupations contribute to variations in sleep habits. This research is relevant to young adults as they transition into professional life, offering insights into the long-term impact of lifestyle choices on sleep health. Understanding these factors can help inform personal wellness strategies, workplace policies, and public health initiatives.

Referenced Datasets

The research projects mentioned above utilized publicly available datasets, demonstrating the importance of data accessibility in research.

Mass Killings in America Dataset: This dataset, maintained by The Associated Press, USA Today, and Northeastern University, provides data on mass killings in the United States from 2006 to the present.
Sleep Health and Lifestyle Dataset: This dataset, available on Kaggle, contains information on various factors affecting sleep patterns and overall health.

The Significance of Interdisciplinary Research

The showcased projects highlight the importance of interdisciplinary research, bringing together expertise from different fields to address complex issues. The study on postpartum mental health and infant sleep combines clinical psychology and statistics, while the study on gun violence incorporates criminology, sociology, and data analysis. The sleep health project draws from health sciences, data science, and lifestyle research.

External Research and Data

The research projects also reference external research and data sources to provide context and support their findings.

The Effect of Social Connectedness on Crime: Evidence from the Great Migration: This study examines the relationship between social connectedness and crime rates, providing insights into the social factors influencing crime.
Census Bureau Quickfacts: Data from the U.S. Census Bureau provides demographic and socioeconomic information for various states, including Texas, California, and Nevada.

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