I am interested in designing high-performance machine learning methods that make sense to humans, and can make sense of humans.
This includes building interpretable latent variable models (NIPS, featured at Talking Machines) and creating structured Bayesian models of human decisions (JAIR).
I have applied these ideas to data from various domains: computer programming education, autism spectrum discorder data, recipes, disease data, 15 years of crime data from the city of Cambridge, human dialogue data from the AMI meeting corpus, and text-based chat data during disaster response.
I graduated with a PhD from CSAIL, MIT (worked with Prof. Julie Shah and Prof. Cynthia Rudin).
Previously, I worked as a developer of MATLAB, and built robots for collaborative navigation.
I am a board member of Women in Machine Learning.