Winter/Spring Special Program on Networks and Inference

2024 Winter/Spring

Winter/Spring Special Program on Networks and Inference

Real-world data, from social networks to protein-protein interactions to technological networks, often exhibit complex dependencies that can be modeled and represented through networks. Generative models of network data are closely related to probabilistic models of interacting particles from statistical physics. One of the main goals of this Winter/Spring 2024 special program is to bring together researchers from computer science, economics, mathematics, physics, and statistics working on problems in networks and inference such as community detection, graph matching, graph representation learning, and learning dynamics on networks. The activities and workshops of the special program will facilitate the exchange of ideas among experts across these diverse fields, creating new collaborations, and leading to novel synergies and advances. 

Organizers

• • Eric Auerbach (Northwestern University)
  • Antonio Auffinger (Northwestern University)
  • Siddharth Bandari (Toyota Technological Institute at Chicago)
  • Julia Gaudio (Northwestern University)
  • Reza Gheissari (Northwestern University)
  • Vishesh Jain (University of Illinois Chicago)
  • Marcus Michelen (University of Illinois Chicago)
  • Lorenzo Orecchia (University of Chicago)
  • Miklos Z. Racz (Northwestern University)
  • Liren Shan (Toyota Technological Institute at Chicago)
  • Elena Zheleva (University of Illinois Chicago)

Graduate Courses

click here to visit IDEAL Winter/Spring 2024 Course Offerings

Weekly Reading Group

TBA