Logistics
Date: May 31, 2024
Location: University of Illinois Chicago, Academic and Residential Complex (ARC) 241, 940 W. Harrison St.
Registration: Click here to register
Description: Graph-structured data presents unique challenges to learning and inference tasks. In the last decade, deep learning methods have revolutionized this field, starting with deep embeddings of graphs, continuing with the rise of the graph neural network paradigm, and ending with current works on deep generative models of graph-structured data. At the same time, the causal inference community has independently developed their own representations of graph-structured data, capturing phenomena, such as social interactions, spatial dependence, network interference, and peer effects. This workshop will explore topics at the intersection of network analysis, machine learning, and causal inference. By bringing together leading experts and practitioners from these areas, the goal of this workshop is to share the latest advances and understand the potential for integration and cross-disciplinary collaboration.
Bryan Perozzi (Google)
Michelle Li (Harvard University)
Murat Kocaoglu (Purdue University)
Pantelis Loupos (University of California, Davis)
Organizers:
- Eric Auerbach (Northwestern University)
- Lorenzo Orecchia (University of Chicago)
- Elena Zheleva (University of Illinois, Chicago)