Bob Carpenter
Location: (New York, NY)
Personal Research Web Page: http://colloquial.com/~carp/
Keywords: Bayesian inference, statistical modeling, bioinformatics
Posted on: Monday, May 9th, 2011
Broad Research Area: AI / Machine Learning / Robotics / Vision, Information Systems / Information Science, Numerical/Scientific Computing / HPC / Data-Intensive Scalable Computing, Scientific/Medical Informatics
Research Interests:
We’re developing general systems for scalable Bayesian inference. We’re exploring a mixture of sampling and point estimation strategies including Hamiltonian Monte Carlo and variational methods.
General purpose tools under development include a general posterior sampler (along the line of BUGS), multiple imputation for missing data, and post-stratification for prediction.
We’re particularly interested in multilevel regression and factor models, with applications to prediction problems in epidemiology, climate modeling, and social science. I’m also interested in large scale probabilistic RNA alignment, expression estimation and pathway modeling.
Other team members include Andrew Gelman, Ben Goodrich, Matt Hoffman, and Michael Malecki.
Contact Information:
carp@lingpipe.com, bob.carpenter@columbia.edu
