This Career Development Plan involves an integrated collection of research and educational activities focused on algorithms for high-dimensional geometric problems.
Geometric computing with high-dimensional data is of crucial importance to many areas of computer science, including machine learning, data mining, databases and information retrieval, computer vision and computational biology. Example problems in this area are: nearest neighbor search, many variants of data clustering, and discovering linear structure of the data (e.g. via Principal Component Analysis (PCA)). Unfortunately, the classical geometric algorithms for many of these problems do not scale well with the dimension. For example, the running times of the classical algorithms for the nearest neighbor search depend exponentially on the dimension, which makes them inefficient for dimension higher than, say, 20. This is unfortunate, since many applications involve number of dimensions anywhere from a few hundred to a few million.
In recent years, new, powerful techniques for solving these problems have been discovered, most notably dimensionality reduction and random sampling in geometric spaces. The algorithms obtained using those techniques enjoy very low (at most linear) dependence on the dimension, at the cost of providing approximate answers. The problems amenable to these techniques include nearest neighbor search, clustering and PCA. However, the algorithmic solutions to these problems still possess (sometimes quite severe) limitations. Our goal is to identify methods for circumventing these limitations and making the algorithms efficient, both in theory and in practice.
The techniques which led to the development of the aforementioned algorithms are new and still not widely known. They include many tools which have been developed during 70s and 80s in the field of mathematics called functional analysis. However, computer scientists became aware of those methods only very recently, and many of the fundamental results are still not widely known or used. Therefore, it is important to make these results accessible to large computer science audience so that they can be successfully used, investigated and developed. In the Education Plan section we outline a plan on how to make them more accessible to students as well as computer scientists.
