The symposium brings together leading experts from universities, industry, and government to explore the state of the art and define a future roadmap in network science. The themes of this year's symposium are domain-informed models and learning and fundamental learning limits of graphs. In order to provide an interactive environment and promote strong interaction among the attendees, the event will be limited to a small group of invited attendees.
The symposium will take place on Tuesday, August 15th - Wednesday, August 16th, 2023 at the MIT Endicott House.
Symposium Highlights
As part of the two-day technical program, topics of interest include:
- Physics-, Knowledge-Informed Models and Learning
- Social Dynamics and Influences
- Explainable, Transparent Graph Learning
- Adversarial Graph Analysis and Learning
- Analysis of Anomalous, Covert, and Hidden Communities
- Inference Under Noise and Uncertainty
- Modeling, Prediction, Control and Optimization
- Recent Advances in Machine Learning on Graphs
- Graph Learning for Combinatorial Optimization
- Various Applications Including Misinformation on Social Media, Cyber Defense,
Bio/Material Design, Climate Change, Resilient Infrastructures and Systems
Symposium Organizers
Chairs
Anu Myne | MIT Lincoln Laboratory
Dennis Ross | MIT Lincoln Laboratory
Rajmonda Caceres | MIT Lincoln Laboratory
Technical Co-Chairs
Benjamin Miller | MIT Lincoln Laboratory
Edoardo Airoldi | Temple & Harvard University
Edward Kao | MIT Lincoln Laboratory
Jason Matterer | MIT Lincoln Laboratory
Lin Li | MIT Lincoln Laboratory
Technical Committee
Alexander Volfovsky | Duke University
Ali Pinar | Sandia National Laboratories
Christopher Long | U.S. Department of Defense
David Martinez | MIT Lincoln Laboratory
Heidi Perry | MIT Lincoln Laboratory
Johan Ugander | Stanford University
Jordan Crouser | Smith College
Nadya Bliss | Arizona State University
Robert Bond | MIT Lincoln Laboratory
Steven Smith | MIT Lincoln Laboratory