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Tuesday, September 26, 2017

Berkeley Distinguished Lectures in Data Science: On Computational Thinking, Inferential Thinking and Data Science

Seminar | September 26 | 4:10-5 p.m. | 190 Doe Library


Michael I. Jordan, Professor, Statistics & EECS, UC Berkeley

Berkeley Institute for Data Science


The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in Data Science is apparent from their sharply divergent nature at an elementary level---in computer...   More >

Thursday, September 28, 2017

GraphXD Seminar: Graph Clustering Algorithms

Seminar | September 28 | 5:30-7 p.m. | 1011 Evans Hall


Tselil Schramm, Simons Institute, UC Berkeley

Berkeley Institute for Data Science


One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk I will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective.

Thursday, October 19, 2017

GraphXD Seminar: Spectral Sparsification of Graphs

Seminar | October 19 | 5:30-7 p.m. | 1011 Evans Hall


Nikhil Srivastava, Dept. of Mathematics, UC Berkeley

Berkeley Institute for Data Science


Many important properties of an undirected graph manifest themselves spectrally in the eigenvalues or quadratic forms of matrices related to the graph. For instance, the connectivity structure, electrical properties, and random walk behavior of a graph are determined by its Laplacian matrix. A spectral sparsifier of a graph G is a sparse graph H on the same set of vertices such that the...   More >