This talk will go over the basics of the PageRank problem, studied initially by the founders of Google, which allowed them to create their search engine by applying it to the internet graph with hyperlinks defining edges. Then, I will explain our new results on the problem for undirected graphs, whose main application is finding local clusters in networks, and is used in many branches of science. We have now algorithms that find local clusters fast in a time that does not depend on the whole graph but on the local cluster itself, which is significantly smaller.

This is joint work with Elias Wirth and Sebastian Pokutta.

About David Martínez-Rubio

David Martínez-Rubio is currently a postdoctoral researcher in the Interactive Optimization and Learning Lab at the Zuse Institute Berlin. He is one of the two leaders of the continuous optimization group, where he does research on topics in Optimization and Learning Theory applied to problems in Networks, Machine Learning and other data science tasks. He obtained his Ph.D. and Master degree in Computer Science and Mathematics from the University of Oxford, and worked for Amazon as an applied scientist. Prior to that, he obtained a double Bachelor in Mathematics and in Computer Science from the Complutense University of Madrid. One of his main research interests is in combining optimization and learning theory techniques to obtain fast provable algorithms to tackle challenging problems in today’s modern data-centric tasks at a large scale.

This event will be conducted in English