Larger, Faster, Random(ized): Computing in the Era of Big Data
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Our capacity to produce and store large sets of data has increased exponentially over the course of the last two decades; the development of algorithms for sifting through it efficiently is somewhat lagging behind. Randomization is being recognized as a powerful tool, whether in constructing models on which algorithms can be tested, or in sampling the data reliably, or in speeding up and optimizing existing algorithms. In particular, basic results from random matrix and random graph theory are being employed at the forefront of scientific computing; often, assembling algorithms and producing theoretical guarantees for them requires a blend of probability, combinatorics, graph theory, numerical analysis, and optimization.
Dr. Dumitriu will speak about three results; the first on Community Detection using the Stochastic Block Model; the second on the spectral gap in bipartite biregular graph and its potential applications; and the third in which randomization is used to achieve a communication-minimizing non-symmetric eigenvalue solver This is joint work with Maryam Fazel, Roy Han, and Amin Jalali; Gerandy Brito and Kameron Harris; respectively, Jim Demmel and Grey Ballard.
Ioana Dumitriu is a Professor of Mathematics at University of Washington. Her research spans numerical analysis and scientific computing, random matrix and random graph theory, and probabilistic and algorithmic combinatorics. She earned her BS from NYU in 1999 and her PhD from MIT in 2003; she spent three years in Berkeley as a postdoctoral Miller fellow, and since 2006 she has been at University of Washington. Among her honors are an Honorable Mention in the Householder Prize for numerical linear algebra (2005) and the Leslie Fox Prize for numerical analysis (2007); she was the recipient of an NSF Career Award (DMS 0847661) and she was named a Fellow of the American Mathematical Society in 2012.