I am an Associate Professor at MIT. I work in the areas of machine learning and statistics. Before coming to MIT, I completed my PhD at UC Berkeley. You can learn more about my background in the following short bio.
I am interested in understanding how we can reliably quantify uncertainty and robustness in modern, complex data analysis procedures. To that end, I'm particularly interested in Bayesian inference and graphical models—with an emphasis on scalable, nonparametric, and unsupervised learning.
Current PhD Students and Postdocs.
Past PhD Students and Postdocs.
- Trevor Campbell, Assistant Professor, University of British Columbia
- Sam Elder, Kebotix
- Jonathan Huggins, Assistant Professor, Boston University
Interested in working with me?
- In Spring 2020, I am teaching 6.435 Bayesian Modeling and Inference. In 2018 and before, this course had the (temporary) number 6.882. Since 2019, it has had the (permanent) number 6.435.
- To apply to work with me as a PhD student, submit your application to MIT EECS. More info at this link.