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SUMMARY:Detecting communities in random graphs
DTSTART:20191210T171500
DTEND:20191210T183000
DTSTAMP:20260407T210827Z
UID:c711f569d063d57028e6ce739b1f79c59019e66734a7887eaab25402
CATEGORIES:Inaugural lectures - Honorary Lecture
DESCRIPTION:Prof. Emmanuel Abbe\,\n\nChair of Mathematical Data Science (S
 B/IC) \nAbstract:\nMuch of our world is organized around categories or co
 mmunities. These may be defined by nature\, e.g.\, species\, genes\, galax
 ies\, or by humans\, e.g.\, society\, products\, knowledge. In many import
 ant cases these communities are not given to us and need to be learned bas
 ed on local interactions or comparisons of the relevant entities. In mathe
 matical terms: one has access to a graph and one wants to extract clusters
  of similar nodes. As with most unsupervised learning tasks\, much of the 
 challenge in community detection is to understand when and how such struct
 ures can be learned. This talk focuses on random graph models\, showing ho
 w techniques ranging from statistics\, information theory\, discrete mathe
 matics and computer science factor in to characterize the fundamental limi
 ts. \nBiography\nEmmanuel Abbé received his Diploma from EPFL in 2003 an
 d his Ph.D. degree from MIT in 2008. He joined Princeton University as an 
 Assistant Professor in 2012 and became Associate Professor in 2016. He was
  a visiting professor at the Simons Institute\, Berkeley\, in 2015 and a v
 on Neumann Fellow at the Institute for Advanced Study\, Princeton\, in 201
 7. Since 2018\, he is a Professor at EPFL in the Department of Mathemati
 cs and in the School of Computer and Communication Sciences\, where he hol
 ds the Chair of Mathematical Data Science.\n 
LOCATION:CO 3 https://plan.epfl.ch/?room==CO%203
STATUS:CONFIRMED
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