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. This year's symposium will focus on social influences on graphs, COVID pandemics, and machine learning on 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.
In light of on-going developments with COVID-19, the symposium will take place virtually on Monday, May 17th - Tuesday May 18th, 2021.
Symposium Highlights
As part of the two-day technical program, topics of interest include:
- Social influences
- Analysis of anomalous, covert and hidden communities
- Inference under noise and uncertainty
- Drug discovery and design
- Pandemic modeling, prediction and control
- Recent advancement in machine learning on graphs
- Novel applications of machine learning on graphs
- Challenges from various applications including bio, cyber, material and social domains
Symposium Organizers
Chairs
Rajmonda Caceres / MIT Lincoln Laboratory
William Streilein / MIT Lincoln Laboratory
Technical Co-Chairs
Edoardo Airoldi / Temple & Harvard University
Edward Kao / MIT Lincoln Laboratory
Jason Matterer / MIT Lincoln Laboratory
Lin Li / MIT Lincoln Laboratory
Technical Committee
Nadya Bliss / Arizona State University
Robert Bond / MIT Lincoln Laboratory
Johan Ugander / Stanford University
Jordan Crouser / Smith College
Timothy Dasey / MIT Lincoln Laboratory
Benjamin Miller / MIT Lincoln Laboratory
Alexander Volfovsky / Duke University
Christopher Long / U.S. Department of Defense
David Martinez / MIT Lincoln Laboratory
Ali Pinar / Sandia National Laboratories
Steven Smith / MIT Lincoln Laboratory