iiD Pop-Up Institute – Learning in Networks

This fall, the Rhodes iiD Pop-Up Institute on Learning in Networks brings together researchers and students from statistics, economics, operations research, computer science, and related fields to advance the theory and practice of both learning from networked data and learning networks from data. The institute focuses on fundamental questions in statistical inference, machine learning, causal inference, and optimization on networks, with particular emphasis on statistical–computational tradeoffs, message-passing algorithms, learning dynamics, and network-based market design. By fostering collaboration across disciplines, the institute aims to develop new mathematical and algorithmic foundations for understanding complex networked systems and their applications. The institute is led by collaborators Jiaming Xu, Alex Belloni, Fan Wei, Galen Reeves, and James Moody.

For more information, contact Jiaming Xu:  jx77@duke.edu.

Learning in Networks Activities

Join our Learning In Networks mailing list to stay up to date on activities.

• Starting September 1, 2026:
  • Reading Group and Seminar Series: Tuesdays, 12pm in 330 Gross Hall; lunch included
  • Tutorial Lecture Series: Tuesdays, 3:30-4:45pm in 318 Gross Hall; Tea provided at 3pm
• Bootcamp:  September 4–5
  • Introductory lectures on core concepts, recent advances, and open problems in learning in networks.
  • Location:  Fuqua School – Geneen Auditorium
• Workshop I:  October 23–24
  • Research talks on statistical–computational tradeoffs, message-passing algorithms, and network inference.
  • Location:  330 Gross Hall
• Workshop II:  November 13
  • Research talks on causal inference, learning dynamics, and network-based market design.
  • Held in conjunction with the WORDS Conference on November 14.
  • Location:  Fuqua School – Geneen Auditorium
• PhD-Level Course:  BA990: Statistical inference on graphs (cross-listed with IDS). Syllabus

More information about Rhodes iiD Pop-Up Institutes can be found here.