# Publications

ARRAY 2019

Visiting the TensorFlow team at Google
brought up the question of how
array programming with Remora differs from
array programming with Numpy and Pandas.
Pandas-style data frames,
a popular tool in data science,
can be expressed in a rank-polymorphic language
as arrays of conventional record data.
Since records were not part of the original design for Remora,
this paper explores the design space
to see how best to enable programming with data frames
in a future extension of Remora.

ARRAY 2018

Working on type inference for Remora hit a particular snag:
when the compiler sees use of a shape-polymorphic function,
it needs to be able to identify the implicit shape arguments
to instantiate the polymorphic function correctly.
While the full theory of shapes (that is, sequences of numbers)
is undecidable,
this paper explains how the structure of Remora
makes it possible for constraint generation to
stay within a fragment of the theory where
the universal quantifiers can be eliminated.

ESOP 2014

This is the beginning of the Remora project,
presenting the semantics of rank polymorphism
embedded in a λ-calculus setting.
The mathematical formalism was developed alongside
an executable model in PLT Redex,
which is linked above.
Later work includes cleaned-up notation
and the introduction of a kind system to
clarify certain restrictions on type quantification.