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.

Y. Annie Liu is Professor of Computer Science at Stony Brook University. Her primary research is in languages and algorithms, especially on systematic methods for design and optimization. The methods are centered around incrementalization—the discrete counterpart of differentiation in calculus. Besides research and service, she also enjoys teaching. She has taught in a wide range of Computer Science areas, and presented over 100 conference and invited talks worldwide. She received her BS from Peking University, MEng from Tsinghua University, and PhD from Cornell University, all in Computer Science.

Annie Liu’s Design and Analysis Research Laboratory has projects in modeling and specification, analysis and verification, design and optimization, code generation, and testing. These projects are for optimizing compilers, interactive environments, real-time and embedded systems, database systems, semantic Web, distributed systems, big data analysis, security, and more. Her awards include a State University of New York Chancellor’s Award for Excellence in Scholarship and Creative Activities.