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

• Madelyn Andersen
• Renato Berlinghieri
• David Burt
• Ryan Giordano (starting as Assistant Professor at UC Berkeley in Summer 2023)
• Tin Danh Nguyen
• Yaroslav Mukhin
• Yunyi Shen
• Miriam Shiffman

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.

Affiliations

• Electrical Engineering and Computer Science (EECS) Department
• Laboratory for Information and Decision Systems (LIDS)
• Statistics and Data Science Center
• Institute for Data, Systems, and Society (IDSS)
• MachineLearning@MIT
• MIT Institute for Foundations of Data Science (MIFODS)

Accessibility
Plain Academic