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 8, Number 2 (2024) Summer 2024
Articles
Enhancing SHAP With Multi-Core Parallelization and Distributed Computation
matthew david, William Jones, and Hayley Horn
Rethinking Retrieval Augmented Fine-Tuning in an evolving LLM landscape
Nicholas Sager, Timothy Cabaza, Matthew Cusack, Ryan Bass, and Joaquin Dominguez
Geospatial Temporal Crime Prediction Using Convolution and LSTM Neural Networks: Enhancing the Las Vegas Cardiff Model
Corey D. Holmes, Christian Orji, and Chris Papesh
Applying Transfer Learning and Existing EEG Datasets to Identify Patients With ALS
Nibhrat Lohia, Chris Mathew, and Garrett Shankel
Enhancing Imputation Accuracy: A Multi-Faceted Approach for Missing Data in Chicago Arrest Records
Steve Bramhall, Jae Chung, and Nicholas Mueller
An Analysis of Drivers of the Federal Funds Rate
Stephen Johnson, Neha Dixit, and Martin Selzer Ph.D.
Data Analysis on Predicting the Top 12 Fantasy Football Players by Position
Alan Abadzic, Jacquelyn Cheun, and Milan Patel
