Rebecca L. Wagner, M.S.

Research Fellow, Data Scientist

The Pennsylvania State University

As a researcher, I use numerous data science methods to examine human memory, including:

harnessing both in-person and virtual methods to generate hypothesis-driven datasets
using R and Python to wrangle, engineer, and analyze intricate datasets
combining human data with supervised and unsupervised ML algorithms to identify complex patterns within data
employing advanced statistical models to disseminate creative, data-driven solutions to researchers and stakeholders alike

Areas of Interest

Hypothesis Testing

Data does not just appear. I pride myself on developing relevant hypotheses, creating scientifically rigorous data collection methods, and ensuring quality data is produced from my projects.

Data Analytics

All data tells a story and I enjoy using analytical methods and visualization tools to translate complex datasets into actionable solutions.

Data Pipelines

No dataset is perfect and I thrive on creating flexible data pipelines using R, Python, and MATLab that can handle data at all stages, including preparation, feature engineering, and analyses.

Machine Learning

I find that marrying ML algorithms with existing data expands research's ability to identify intricate patterns and create predictive models that keep organizations at the forefront of what is next.