I am co-organizing NIPS 2016 Worshop on Interpretable Machine Learning for Complex Systems.
I am interested in designing high-performance machine learning methods that make sense to humans, and can make sense of humans.
Here is a short writeup about why I care.
This includes building interpretable latent variable models (featured at Talking Machines) and creating structured Bayesian models of human decisions .
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 have co-organized ICML 2016 Worshop on Human Interpretability in Machine Learning (WHI).
I am an executive board member of Women in Machine Learning.