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Cameron Nicholas Musco

Cameron Nicholas Musco's picture
Research Category: 
Analytics and Applications
Primary CSAIL Advisor: 
Nancy Lynch
Short Bio: 

I am a second year Ph.D. student in the Theory of Computation Group at MIT CSAIL. I am lucky to be advised by Nancy Lynch and partially supported by an NSF Graduate Research Fellowship. I study algorithms and am especially interested in linear algebraic computation, data analysis, and spectral graph theory. Recently I have been focused on linear sketching and dimensionality reduction, and applications to modern computational settings, such as distributed and streaming data processing. Before MIT, I studied Computer Science and Applied Mathematics at Yale University and worked as a web developer at Redfin.

Description of Research: 
A data 'sketch' is a small representation of a large dataset that allows us to approximately solve data analysis and machine learning problems on the original dataset. In the past, we have studied sketching algorithms for problems such as large scale linear regression and graph approximation. Recently, our work has focused on sketches for clustering and low rank approximation on very high dimensional data. We have achieved a number of new theoretical bounds showing that sketching algorithms based on random projection, principal component analysis, and feature sampling can be used to significantly reduce dimension, while still maintaining enough information to find near optimal clusterings and lowrank approximations. In the future, we hope to evaluate the performance of these algorithms in practice, and extend our techniques to other relevant data analysis problems.