Professor, Computer Science and Engineering
High-dimensional statistics, clustering, algorithms for finding underlying patterns in high-dimensional data, machine learning Professor Sanjoy Dasgupta develops algorithms for the statistical analysis of high-dimensional data. Such data is now widespread, in domains ranging from environmental modeling to genomics to web search. The geometry of high-dimensional spaces presents unusual challenges; many traditional statistical procedures were developed with one- or two-dimensional data in mind and do not scale well to this modern context. Some of them are very inefficient; others give poor results because of counter-intuitive effects in high dimension. Dasgupta has developed the first provably correct, efficient algorithms for a variety of canonical statistical tasks, especially related to clustering (grouping) data. He is one of the few machine learning researchers whose work combines algorithmic theory with geometry and mathematical statistics. He adds a strong theoretical focus to UCSD's CSE artificial intelligence and bioinformatics groups.
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