Geometric spaces arise in computer science through a number of avenues. The most obvious of these occurs when the input data for a problem possesses an inherent metric structure, like the hop-distance between nodes in a network, or the similarity distance between pairs of genomic sequences. But there are other, more subtle examples, like the geometry of sparse vectors, which arises prominently in coding theory, signal recovery, and quantum information, or the effective resistance distance between nodes in an electrical network, which has proven to be a powerful algorithmic tool in attacking both algebraic and combinatorial problems.
Perhaps most strikingly, in the setting of combinatorial optimization, high-dimensional geometry often presents itself in an unexpected and profound manner. A basic example is the use of convex optimization in the solution---exact or approximate---to a variety of combinatorial problems. In many cases, a problem with an a priori purely combinatorial structure is shown to involve rich geometric phenomenon. Furthermore, we now realize that often this structure is inherent and fundamental, in the sense that any solution to the problem must confront its geometric core.
The PI will seek to understand these connections and develop new algorithmic techniques to exploit them. This work will employ techniques from high-dimensional geometry and probability, functional analysis, spectral geometry, and combinatorics to attack problems at the forefront of computer science. This includes addressing fundamental gaps in our understanding of theoretical issues, as well as developing solutions to practical problems that arise from the need to analyze and manipulate massive data sets. To achieve these goals, the investigator intends to address central, important open problems in the fields of approximation algorithms, high-dimensional information theory, and discrete asymptotic convex geometry.
