This talk gives an overview of partial evaluation and incremental
computation as meta frameworks, and of the power of systematic
specialization and incrementalization transformations for achieving them.
In particular, optimization by incrementalization allows efficient
algorithms to be derived systematically and generated automatically from
high-level specifications, for problems in widespread and diverse
applications. This helped the design of more powerful, higher-level
languages, including DistAlgo, a language for distributed algorithms. All
these further propelled the study of logic and constraints, the foundation
of knowledge representation and reasoning, where paradoxes are solved to
arrive at agreed-upon meanings.
Bio:
Annie Liu's primary research is in languages and algorithms,
especially on systematic design and optimization, centered around
incrementalization---the discrete counterpart of differentiation in
calculus. Her current research focus is on powerful languages and
efficient implementations for secure distributed programming and for
declarative system specifications.
https://www.cs.stonybrook.edu/
