research position on NASA-funded project, synthesis of learning alg.

[Wray Buntine]

Immediate opening for late summer position, full or half-time
funded by NASA Ames Research Center for good masters, in-progress PhD 
or postdoc.  American citizens or resident aliens only can apply.
Introduction to the research for the project can be found 
in the groups pending KDD'99 paper available at:
	http://www-cad.eecs.berkeley.edu/~wray/kdd99.ps
and relevance of broader research goals at:
	http://www-cad.eecs.berkeley.edu/~wray/Mirror/x2009.pdf
This is a unique opportunity to work with leading experts in
both data analysis and automated software engineering
	http://ic.arc.nasa.gov/ic/projects/amphion/index.html	
developing NASA's future intelligent systems.  Position
is short term and has potential for growth, and ideal for
student seeking late summer work.

The Task
========
Applicant should have knowledge of statistical
algorithms for data analysis, and familiarity with their application
and their implementation.  Applicant will support NASA Ames' automated
software engineering experts in their development of the system,
and in testing the system, and co-develop the system with the
data analysis expert. 

The System
==========
Code synthesis is routinely used in industry to generate GUIs, for database
support, and in computer-aided design, and has been successfully used in
development projects for scheduling.  Our research applies synthesis to
statistical, data anlysis algorithms.  For synthesis, we use a specification
language that generalizes Bayesian networks, a dependency model on
variables. Using decomposition methods and algorithm templates, our system
transforms the network through several levels of representation into
pseudo-code which can be translated into the implementation language of
choice.  Algorithm templates are been developed for a variety of
sophisticated schemes including EM, mean-field, maximum aposterior, and 
iterative-reweighted least squares.  The transformation system is based on a
term-rewriting core with the capacity for symbolic simplification and
differentiation of sums and products over vectors, matrices and delta
functions.  We are currently developing a back-end optimizer for Matlab.

Requirements 
============ 
Applicant should be a keen and experienced coder with a knowledge of
the relevant data analysis algorithms and their implementation.  Code is
written in a mixture of Prolog (for the synthesis) and Lisp (for the
back-end), and basic exposure to these languages is necessary.
The back-end is Matlab so exposure here is useful too.
Development on a Linux platform.  Applicant should be willing
to learn new languages/environments.

Please contact Wray Buntine for further information at:
	wray at ic.eecs.berkeley.edu

Applications
============
Email your resume or a URL to Wray Buntine at the above
address.  Microsoft Word documents definitely discouraged.
No deadline but sooner better!