Collaborative Research: Graph Learning, Generation, and Optimization with Highly Expressive Graph Neural Network Models

Information

  • NSF Award
  • 2415227
Owner
  • Award Id
    2415227
  • Award Effective Date
    9/1/2024 - 3 months ago
  • Award Expiration Date
    8/31/2027 - 2 years from now
  • Award Amount
    $ 160,338.00
  • Award Instrument
    Continuing Grant

Collaborative Research: Graph Learning, Generation, and Optimization with Highly Expressive Graph Neural Network Models

In today's data-driven world, many complex systems can be represented as interconnected networks or graphs. This project aims to develop new methods for analyzing, generating, and optimizing these graph structures, with potential applications in areas such as social network analysis and molecular design. By improving the ability to learn from and work with graph-structured data, the project is expected to provide new tools for researchers across various scientific fields. The proposed research contributes to advancements in areas such as drug discovery, network analysis, and modeling of physical systems, offering new ways to approach complex problems in these domains. This project also offers research training opportunities for undergraduate and graduate students.<br/><br/>The project focuses on four main research areas: (1) developing more expressive and efficient graph neural networks, (2) creating improved generative models for graphs, (3) applying graph learning techniques to optimization problems, and (4) exploring the use of graph neural networks for discovering physical relations. The interconnected research thrusts aim to improve the capabilities of machine learning models based on graphs, laying the groundwork for solving complex graph-related challenges. The project will produce new mathematical and statistical tools, theoretical frameworks, and assessment methods for learning from graphs. The work is expected to advance graph learning techniques and their applications in scientific fields, providing researchers with new ways to handle data structured as graphs.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

  • Program Officer
    Tapabrata Maititmaiti@nsf.gov7032925307
  • Min Amd Letter Date
    8/20/2024 - 4 months ago
  • Max Amd Letter Date
    8/20/2024 - 4 months ago
  • ARRA Amount

Institutions

  • Name
    Illinois Institute of Technology
  • City
    CHICAGO
  • State
    IL
  • Country
    United States
  • Address
    10 W 35TH ST
  • Postal Code
    606163717
  • Phone Number
    3125673035

Investigators

  • First Name
    Maggie
  • Last Name
    Cheng
  • Email Address
    maggie.cheng@iit.edu
  • Start Date
    8/20/2024 12:00:00 AM

Program Element

  • Text
    CDS&E
  • Code
    808400

Program Reference

  • Text
    COMPUTATIONAL SCIENCE & ENGING
  • Code
    9263