Thrilled to share that my first first-authored publication is officially out (soon to be in press at Social Cognition)! 🚀 "From Data to Discovery: Unsupervised Machine Learning in Social Cognition". 📄 OSF preprint:

🎓 Paper: 📺 YouTube Tutorials: 📦 GitHub: #RStats #MachineLearning #SocialPsych #AcademicTwitter #DataScienc.

This paper was a labor of love and I’m so grateful to my brilliant co-authors @MMamakos_ ,.@sylviapperry, and @TesCharlesworth for their support, feedback, and generosity throughout the process.

We also include extensive annotated R code, visual walkthroughs, and a free YouTube tutorial series to make this genuinely hands-on. If you’re curious about how UML can help you uncover new patterns in your data—check it out!.

Each section walks through: .- what the method is .- how to implement it in R .- how to interpret the results .- real examples using Project Implicit data.

We focus on 4 key UML techniques: .- K-means clustering .- DBSCAN . - Principal Component Analysis (PCA) .- Market Basket Analysis (MBA).

Social cognition is drowning in big, complex data—but we often use the same familiar methods. Unsupervised Machine Learning (UML) gives us tools to explore patterns without predefined outcomes. We wrote this paper to demystify UML and help researchers use it meaningfully.

🧵 A brief thread on what this tutorial paper is about & why I’m excited about it:.

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RT @felix_chenwei: I'm extremely honored to be recognized by the community with this award and the invitation for long abstract! . Thank yo….

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RT @wendybmendes: Applications being accepted for undergraduate summer internships in Affective Science at Yale University. See https://t.c….

RT @kevinwildes: Jalen Hurts .who was in the Super Bowl 23 months ago.who hasn't lost a game he finished since September.who trades persona….