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
- Raj Agrawal, Principal Scientist, ArbiLex
- Trevor Campbell, Assistant Professor, University of British Columbia
- Sameer Deshpande, Assistant Professor, University of Wisconsin–Madison
- Sam Elder, Machine Learning Scientist, Kebotix
- Jonathan Huggins, Assistant Professor, Boston University
- Mikołaj Kasprzak, Marie Skłodowska-Curie (Global) Fellow
- Lorenzo Masoero, Applied Research Scientist, Amazon
- William Stephenson, Member of Technical Staff, MIT Lincoln Labs
- Brian Trippe, Postdoctoral Fellow, Columbia University
Interested in working with me?
- In Spring 2023, I am teaching 6.7830 Bayesian Modeling and Inference. (Note: MIT EECS renumbered all of its courses for the 2022/2023 academic year; in previous years, this course was listed as 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.