Faculty Candidate Seminar|
New Algorithms for High-Dimensional Data
Thursday, February 16, 2017|
4:00pm - 5:00pm
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About the Event
A popular approach in data analysis is to represent a dataset in a high-dimensional feature space, and reduce a given task to a geometric computational problem. However, most of the classic geometric algorithms scale poorly as the dimension grows and are typically not applicable to the high-dimensional regime. This necessitates the development of new algorithmic approaches that overcome this "curse of dimensionality". In this talk I will give an overview of my work in this area.
Ilya Razenshteyn is a graduate student at the Theory of Computation group of MIT CSAIL advised by Piotr Indyk. He is broadly interested in the theory of algorithms for massive data with a bias towards algorithms which have the potential of being useful for applications. More specific interests include: similarity search, sketching, metric embeddings, high-dimensional geometry, streaming algorithms, and compressed sensing. Ilya graduated with M.Sc. in Mathematics from Moscow State University back in 2012. His awards include Akamai Presidential Fellowship and Simons Foundation Junior Fellowship.
Open to: Public