bringing self-advocacy skills to machine learning
yreg @ uw.edu
I'm a PhD student at the University of Washington iSchool working with Emma Spiro in the Center for an Informed Public. I am an NSF GRFP Fellow. I am also a member of the
MD4SG Data Economies Working Group and I consulted on Code.org's AI curriculum for middle schoolers!
I study the intersection of self-advocacy and machine learning literacy. I use co-liberation and implement interventions for marginalized communities to advocate for their needs in an ML-driven world; from resisting diet culture in the newsfeed to helping students understand admissions metrics.
I use they/them pronouns because I'm a nonbinary person. It takes practice, and is also very important to me! I believe in you.
My autism guides so much of what I do. You can read my primer on autism here! My special interests include ASMR 🎧, machine learning algorithms 👨💻, honeybees 🐝 (and other bugs), and ocean animals 🐙.
I love to infuse joy and play into all things that I do, including a series of lessons for RStudio called Data Science for Software Engineering. Here is a fun tutorial I built for teaching Linear Regression on your own data, called LearnMyData.
Or an introduction to the Fragile Families Project for new Data Science students.
I write about machine learning, autism, therapy, computing, teaching, and more on my Medium profile. May we all believe in a better world, and spark joy in each other to make that possible.
Places you can find my digital presence:
Using their own Facebook data, participants learn how User-Based Collaborative Filtering works and then advocate for themselves for situations where the algorithm recommends something harmful. Also adapted as a: ReadyAI lesson for highschoolers.
Documenting how users on r/instagram have discussed the Instagram algorithm since 2013. Building a classifier with scikit to sort posts into 'complaining' about the algorithms and 'explaining' the algorithms and documenting themes over time of algorithmic sensemaking, concerns, and folk theories.
Collaboration with code.org on a Model AI assignment that teaches k-nearest neighbors to middle schoolers using the zombie apocalypse!
Just like algorithms take in data to shape the truth, so does the human brain. This is a journey through common algorithms in the modern world, and helps you use lessons from those algorithms to retrain your brain for self-love. Finalist for the Gradient Prize.
Univesity students learn linear regression and gradient descent on their own grade history, then advocate for themselves when the model makes a wrong prediction.
Discussing work from my Rstudio Internship about teaching key data science skills to software engineers
You can download my art to use in presentation slides here.
