I am broadly interested in probabilistic modeling, and in particular, theory and methods for robust, scalable, and nonparametric Bayesian modeling. Trevor Campbell, Diana Cai, Tamara Broderick. Diana Cai, Trevor Campbell, Tamara Broderick. Priors on exchangeable directed graphs. Diana Cai, Nate Ackerman, Cameron Freer.

I am currently a computer science post-doctoral research fellow at Harvard University, under the supervision of Finale Doshi-Velez. in computer science at Brown University in May 2016, advised by Erik Sudderth.

Before moving to MIT, I completed my PhD at UC Berkeley with Michael I. You can learn more about my background in the following short bio. I am a guest editor for a special issue on Bayesian Nonparametrics. In the journal Statistics and Computing.

I am a research scientist at Disney Research Pittsburgh, where I lead the statistical machine learning group. From 2014 to 2016 I was a postdoctoral researcher with David Blei. And a PCCM Postdoctoral Fellow at Princeton University. At the Institute for Theoretical Physics. At the University of Cologne, where I was supported by the German National Merit Scholarship. Where my research centers on probabilistic mo.

Welcome to my homepage! Grenoble - Rhône-Alpes in the. Prior to that, I was a postdoctoral fellow at. New directions in Bayesian nonparametrics. More in my CV, in. Bayesian parametric and nonparametric Statistics.

PhD Candidate in Computer Science. I study statistical machine learning, focusing on spatiotemporal models and scalable inference methods. I like to be application-driven; my applied work ranges from problems in astronomy to healthcare to sports analytics. I am a member of the Harvard Intelligent Probabilistic Systems. I also work with Luke Bornn.