Description:

April 9th and 10th
Description: 

Real-world networks often exhibit a hidden structure, which we wish to infer. For example, many networks exhibit community structure. Inferring communities is a valuable tool in network analysis; community detection has been used in a wide array of applications including recommender systems (e.g. Netflix), webpage sorting, fraud detection, and neurobiology. Inspired by these real-world networks, researchers in probability, statistics, information theory, and machine learning have studied structure recovery problems in random graph models. In addition to community detection, problems of this type include graph matching, recovery of planted subgraphs, and inference of graph properties. This workshop will bring together leading experts in the field, and both local and external participants, with the goal of sharing the latest advances and launching new collaborations.

Form to register:
https://docs.google.com/forms/d/e/1FAIpQLSfZoMhFJMR00yQDSmYQYg33qQ-lpKtnGBm3jyV3XMXzq7Sgrg/viewform
 
Speakers:
Elchanan Mossel (MIT)
Tselil Schramm (Stanford University)
Alex Wein (UC Davis)
Jiaming Xu (Duke University)
 
Logistics:
Date: April 9th -10th
In-person Location: Northwestern University: Mudd Library 3rd floor, 2233 Tech Drive, Evanston