David Blei writes:
The thrusts are (a) scalable inference and (b) model checking. We
will be developing new methods and implementing them in probabilistic
programming systems. I am open to applicants interested in many kinds
of applications and from any field.
“Scalable inference” means black-box VB and related ideas, and “probabilistic programming systems” means Stan! (You might be familiar with Stan as an implementation of Nuts for posterior sampling, but Stan is also an efficient program for computing probability densities and their gradients, and as such is an ideal platform for developing scalable implementations of variational inference and related algorithms.)
And you know I like model checking.
Here’s the full ad:
===== POSTDOC POSITIONS IN PROBABILISTIC MODELING =====
We expect to have two postdoctoral positions available for January 2014 (or later). These positions are in David Blei’s research group in the Computer Science Department at Princeton University. They are one-year positions with likely renewal to two years. They are for doing basic research in probabilistic modeling.
We will have two main research thrusts:
(a) Developing new scalable methods of approximate posterior
inference. We are interested in developing generic variational
methods for massive data sets and streaming data sets. For example,
see our recent work on stochastic variational inference (Hoffman et
al., 2013) and nonconjugate variational inference (Wang and Blei,
(b) Developing new methods for calculating model fitness and new ways
of diagnosing model misfit. We are interested in developing modern
methods related to predictive sample re-use (Geisser, 1975) and
posterior predictive checks (Rubin, 1984; Meng, 1994; Gelman et al.
We will implement these ideas in modern probabilistic programming
systems and exercise them in several problem domains. (Though the
work is for general methodological research, we encourage applicants
who are already interested in specific applied problems.) More
broadly, our goal is to tighten the probabilistic modeling
pipeline—posit a model, estimate a posterior, check the model,
revise the model—in the service of scientific and technological
Applicants should have a PhD and experience with applied probabilistic
modeling. Our research will be in statistical machine learning, but
we happily will consider applicants fields outside of Computer Science
and Statistics (e.g., Physics, Biology, Social Sciences, Astronomy,
Send your CV to firstname.lastname@example.org and arrange to have two
letters of reference sent to the same address. Optionally, you may
also include a research statement.