I'm a PhD student at the University of Washington iSchool working with Emma Spiro in the Data Lab. I am an NSF GRFP Fellow (which is pretty dope!). 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 have struggled for most of my life with feeling misunderstood, and understand that it is no longer my shame to carry!
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.
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.
Univesity students learn linear regression and gradient descent on their own grade history, then advocate for themselves when the model makes a wrong prediction.
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.
Just like algorithms take in data to shape the truth, so does the human brain. This book takes you on a journey through common algorithms in the modern world, and helps you use lessons from those algorithms to retrain your brain for self-love.
Discussing work from my Rstudio Internship about teaching key data science skills to software engineers