Seeking PhD students and postdocs interested in an elegant combination of functional programming and big-iron style numeric computing. (This project is, in truth, motivated by machine learning: I want to make it easy to implement complicated models, and to run them efficiently.)
Functional Programming and Automatic Differentiation
We are adding exact first-class derivative calculation operators (Automatic Differentiation or AD) to the lambda calculus, and embodying the combination in a production-quality optimising compiler.
Our research prototype compiler generates object code competitive with the fastest current systems, which are based on FORTRAN. We are seeking PhD students and postdocs with interest and experience in relevant areas, such as programming language theory, numeric computing, machine learning, numeric linear algebra, differential geometry; and a burning drive to help lift big iron numeric computing out of the 1960s and into a newer higher order. Specific sub-projects
include: compiler and numeric programming environment construction; writing, simplifying, and generalising numeric and machine learning algorithms through the use of type theory and AD operators; and associated type/lambda calculus/PLT/real computation issues.
Project headquarters: Hamilton Institute, NUI Maynooth, Ireland, http://www.hamilton.ie/.
Applications and queries to:
Barak A. Pearlmutter firstname.lastname@example.org