About me

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 (plaintext) short bio.

In my research, 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

Interested in working with me?

  • In Spring 2021, 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. I can also advise PhD students accepted to other appropriate programs at MIT; e.g., I have advised PhDs in Math and CSB.
  • To apply to work with me as a postdoc, email me your CV (pdf), a statement of research interests, a pdf of 1 (or 2) of your most significant publications, and the contact details (including email addresses) of two references.


Plain Academic