Applied Data Science & Analytics | Howard University
This project is maintained by kirstencalloway
My work sits at the intersection of positive psychology, data science, and digital humanities. I approach AI critically and carefully, focusing on how we build systems — psychological frameworks, AI tools, research methodologies — that actually see the full complexity of human identity rather than forcing people to code-switch, mask, or shrink themselves to be understood. I’m interested in its potential to support people with language barriers, learning differences, or limited access to educational resources — but only when designed with deep attention to ethics, environmental impact, and the communities most affected by algorithmic systems. My work examines how technology can be designed to honor identity complexity and serve communities historically excluded from both psychological research and technological development.
Descriptive and inferential analysis of character strength patterns across three racial groups using VIA Institute data (n = 7,047). Includes EDA, two-way ANOVA, correlation analysis, constellation analysis, and SMOTE implementation for class imbalance.
Multiclass classification predicting racial group membership from 24 character strength features. Compares Logistic Regression and Random Forest with class weighting, stratified cross-validation, and feature importance analysis.
Narrative essay connecting statistical and machine learning findings to the systemic erasure of multiracial identity across psychological assessment, clinical algorithms, and AI systems.
Interactive Flourish storyboard exploring character strength constellations across racial groups.