This Project Grant award of $500,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of an innovative deep learning framework that combines physics-informed principles with scientific domain-adapted generative diffusion models. The goal is to overcome key challenges in scientific inverse design and accelerate scientific discovery, with a focus on advancing the frontiers of artificial...
This Project Grant award of $175,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of artificial intelligence. The project develops new statistical and computational methods to address fundamental challenges related to robustness, adaptivity, and structure in modern algorithms and statistical procedures. The research...
This $207,737 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop a new class of machine learning models called "Programmatic Foundation Models" that can efficiently analyze large-scale satellite, aerial, and ground imagery. The goal is to create interpretable, robust AI models that can understand global and local phenomena from images, providing insights...
This Project Grant award for $557,158.00, funded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070), supports research to devise novel mathematical operators that address the computational bottlenecks of graph-based artificial intelligence (AI) applications. The project aims to unlock sustainable and scalable performance for modern AI-based applications, such as autonomous systems, traffic forecasting, social media, drug...
This $150,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports collaborative research to develop new methods for analyzing, generating, and optimizing graph-structured data. The project aims to create more expressive and efficient graph neural network models, improved generative models for graphs, and apply graph learning techniques to optimization problems and physical systems modeling. The...
This Project Grant award, valued at $500,000.00 and awarded on June 15, 2025, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The project, titled "ACED: ACCELERATING MATERIALS DISCOVERY BY LEARNING WITH PHYSICS-INFORMED CONSTRAINTS," aims to revolutionize materials discovery by integrating fundamental physical principles into machine and deep learning models. The goal is to...
This Project Grant award, with a total funding of $200,000.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The goal of this 3-year project, starting on March 1, 2025, is to investigate spatial-temporal data analysis in AI-enabled Internet-of-Things (AIoT) systems by advancing graph signal processing and graph learning techniques. The key research thrusts include: (i) developing novel topology sampling and...
This $199,040 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop new classes of computational algorithms that combine the benefits of direct computer simulations and the speed of machine learning predictions. The project, titled "XTRIPODS: HYBRID SCIENCE-MACHINE LEARNING SOLVERS FOR NANOPHOTONICS AND METAMATERIALS," will embed scientific knowledge into the machine learning...
This $400,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a new framework and tools for advancing data-centric artificial intelligence (AI) through generative approaches to feature space reconstruction. The project aims to transform the traditional way of constructing feature spaces by using deep generative learning instead of manual or classical discrete search...
This Project Grant award, with a total funding of $549,999, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The primary objective is to develop a time-sensitive large model training platform for dynamic data analytics, enabling real-time adaptability of large-scale deep learning models across various applications such as climate modeling, traffic management, and virtual infrastructure twins. The...
This Project Grant award of $500,000 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to revolutionize spectral analysis using state-of-the-art artificial intelligence (AI). The project will develop a novel chemistry-informed, multi-modal deep learning framework to enable automatic, accelerated, and accurate translation between spectral signals and molecular structures. This will streamline and simplify spectral analysis for applications in scientific research, national healthcare, national security, education, and other domains. The project will leverage foundation models and language translation techniques to create a universal toolkit for rapidly converting between numerical spectral data and molecular fingerprints. This work has broad potential impact across fields like chemistry, biology, medicine, astronomy, materials science, and environmental science where spectral analysis is crucial. The award duration is from Jul 1, 2025 to Jun 30, 2027, and no sub-awards are planned.