RooStats is a set of statistical tools primarily used for high-energy physics. We are pushing the limits of big data and statistical modeling, so this experience is almost guaranteed to give you a unique perspective -- and potentially the gratification of contributing to the discovery of new fundamental particles and forces.

Our code development is almost entirely in C++, though there are some projects that can be done in Python. The C++ constructs are not particularly advanced, instead the emphasis is on a clean mapping between the code and statistical concepts and the implementation of statistical algorithms that can work on statistical models of arbitrary complexity.

Below are projects that range from hard-core computational statistics to unit testing and code profiling. We also have some projects related to GUIs and more human interfaces to statistical tools.

Many of these project ideas are rather vague. If one seems interesting, you are encouraged to contact <roostats-development@cern.ch> or kyle dot cranmer at nyu edu.

Note, the mentor is in contact with a few statisticians that can help navigate the literature associated with graphical models etc.

Expected results: A C++ class class structure for finding and storing the graphical model based on our current representation of a probability density function. A C++ class to implement one or more of the sampling algorithms based on the graphical model, analogous to the Metropolis-Hastings class: http://root.cern.ch/root/html532/RooStats__MetropolisHastings.html

Knowledge Prerequisite: C++, basic statistics, basic graph theory

Expected results: A unit testing suite that can run in a nightly build system. Some stress-tests that can be used to benchmark the various statistical algorithms.

Knowledge Prerequisite: C++, unit testing methodology

Expected results: Profiling of various statistical algorithms on problems of different complexity. The complexity can be characterized in terms of number of variables, number of entries in the data, number of iterations of particular operations, etc. ideally, the leading inefficiencies are tracked down and specific code optimizations are suggested and/or implemented.

Knowledge Prerequisite: valgrind, cachgrind, and/or equivalent debugging and profiling tools.

There are more possibilities for using a GUI to construct, edit, and combine statistical models.

Expected results: A GUI to do something useful.

Knowledge Prerequisite: GUIs, C++

Expected results: A GUI that can convert a sketch into a PDF

Knowledge Prerequisite: GUIs, C++

Expected results: Modifications to existing code to support running different chains in parallel. Implementation of standard MCMC convergence measures.

Knowledge Prerequisite: C++, basic statistics

-- KyleCranmer - 05-Mar-2012

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Topic revision: r2 - 2012-03-06 - KyleCranmer

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