SMU Data Science Review
The SMU Data Science Review is a repository of SMU Masters student work in the field of data science as well as an electronic journal that promotes data-driven scientific discovery and welcomes experimental and theoretical research on advanced data science technologies and their real world applications.
See the Aims and Scope for a complete coverage of the journal.
Current Issue: Volume 7, Number 3 (2023)
Articles
Predicting Land Reclamation of Bond Released Surface Mines
Kendall Scott, Austin Webb, Tadd Backus, and Robert Slater
The Impact of the COVID-19 Pandemic on Faculty Productivity and Gender Inequalities in STEM Disciplines
Monnie McGee, Raag Patel, Roslyn Smith, and Satvik Ajmera
A Prompt Engineering Approach to Creating Automated Commentary for Microsoft Self-Help Documentation Metric Reports using ChatGPT
Ryan Herrin, Luke Stodgel, and Brian Raffety
Impact of COVID-19 on Recruitment of High School Athletes to DI Track and Field
Christopher Haub, Jon Paugh, Alonso Salcido, and Monnie McGee
Differentiation of Human, Dog, and Cat hair Fibers using DART TOFMS and Machine Learning
Laura Ahumada, Erin R. McClure-Price, Chad Kwong, Edgard O. Espinoza, and John Santerre
Investigation into a Practical Application of Reinforcement Learning for the Stock Market
Philip Traxler, Sadik Aman, Will Rogers, and Allyn Okun
Deep Learning Image Analysis to Isolate and Characterize Different Stages of S-phase in Human Cells
Kevin A. Boyd, Rudranil Mitra, John Santerre, and Christopher L. Sansam
Identifying Locations of Drug Overdose in Las Vegas to Implement the Cardiff Violence Prevention Model
John Girard, Shikha Pandey, Zack Bunn, Chris Papesh, Jacquelyn Cheun PhD, and Ying Zhang
Ohio Recovery Housing: Resident Risk and Outcomes Assessment
Elyjiah Potter and Bivin Sadler
