Project Grant 2509011

Award Date 7/1/25
Completion Date 6/30/28
Dollars Obligated $187K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Cambridge, MA 02139, USA
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This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $187,000.00 to the Massachusetts Institute of Technology (MIT) to develop theoretical foundations for improving graph neural networks (GNNs), which are widely used machine learning models for graph-structured data. The key objectives are to:

  1. Develop GNN architectures for solving quadratic programming optimization problems,
  2. Analyze the expressivity of GNNs on graphs with bounded cycles, and
  3. Design new approaches to mitigate the oversmoothing phenomenon in deep GNNs.

The research will draw on techniques from graph theory, optimization, and neural network theory to advance the theory and practice of efficient and scalable graph learning. The award also involves undergraduate and high school student mentorship and educational programs. This award supports NSF's mission to strengthen the nation's scientific enterprise and promote scientific progress.

Generated 7/15/25, 10:08 AM