Ray Opoku

Data Scientist University of Florida

Gainesville FL

Ray Opoku studies artificial intelligence, STEM education and equitable machine learning in health and learning systems.

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Biography

Ray Opoku is a data scientist whose work bridges artificial intelligence, healthcare and education. His research examines how AI can be applied responsibly to improve learning and clinical decision-making while addressing issues of fairness and equity. He focuses on algorithmic bias, predictive modeling and informal STEM learning, with particular interest in how underrepresented youth engage with AI.

Areas of Expertise

Educational Technology and Learning Analytics

Predictive Analytics

Artificial intelligence in education and healthcare

Machine Learning

Algorithmic Fairness and Bias

Articles

Unveiling Accuracy-Fairness Trade-Offs

Journal of Learning Analytics

Opoku, et al.

2025-07-31

While high-accuracy machine learning (ML) models for predicting student learning performance have been widely explored, their deployment in real educational settings can lead to unintended harm if the predictions are biased. This study systematically examines the trade-offs between prediction accuracy and fairness in ML models trained on the widely used Open University Learning Analytics Dataset.

Care coordination and patient safety outcome: a graph-based approach

NPJ Health Systems

Chen, et al.

2025-05-08

Predicting postoperative adverse events and managing the associated risk factors is crucial for patient safety. Care coordination, also known as provider team interactions, significantly impacts outcomes, yet few studies have explored this link and applied it to risk prediction. To address this, Medical Heterogeneous Graphs for Patient Safety analysis, a novel graph-based framework that simultaneously models complex relationships among patient characteristics, provider interactions, and patient transfer records was proposed in this study.

Using machine learning techniques to investigate learner engagement with TikTok media literacy campaigns

Taylor & Francis

Wusylko, et al.

2024-01-11

Social media has the unique capacity to expose many learners to media literacy instruction via targeted campaigns. Investigating learner engagement and reaction to these efforts may be a fruitful endeavor for researchers that can inform the design of future campaigns. However, the massive datasets associated with social media posts are difficult, and often impossible, to analyze with traditional qualitative methods. This study seeks to address this problem by leveraging machine learning techniques to collect and analyze Big Data from two different media literacy campaigns on the youth-oriented social media platform TikTok.