We present a new undergraduate ML course at our institution, a small liberal arts college serving students minoritized in STEM, designed to empower students to critically connect the mathematical foundations of ML with its sociotechnical implications. We propose a “framework-focused” approach, teaching students the language and formalism of probabilistic modeling while leveraging probabilistic programming to lower mathematical barriers. We introduce methodological concepts through a whimsical, yet realistic theme, the “Intergalactic Hypothetical Hospital,” to make the content both relevant and accessible. Finally, we pair each technical innovation with counter-narratives that challenge its value using real, open-ended case-studies to cultivate dialectical thinking. By encouraging creativity in modeling and highlighting unresolved ethical challenges, we help students recognize the value and need of their unique perspectives, empowering them to participate confidently in AI discourse as technologists and critical citizens.

I’m an Assistant Professor of Computer Science at Wellesley College, where I lead the Model-Guided Uncertainty (MOGU) Lab. My research focuses on developing new machine learning methods to advance the understanding, prediction, and prevention of suicide and related behaviors.

Before joining Wellesley, I was a postdoctoral fellow at the Nock Lab in the Department of Psychology at Harvard University and Mass General Hospital. I completed my Ph.D. in Machine Learning at the Data to Actionable Knowledge Lab (DtAK) at Harvard, working with Professor Finale Doshi-Velez. I had the pleasure of interning with the Biomedical-ML team at Microsoft Research New England (Summer 2021). Lastly, I received a Master’s of Music in Contemporary Improvisation from the New England Conservatory (2016) and a Bachelor’s of Arts in Computer Science from Harvard University (2015).