This $224,910 federal Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of scalable and automated software to simulate active dynamics and infer the laws governing this behavior from experimental data. The project aims to empower researchers to reliably extract insights from noisy observations of active matter systems, such as collections of cells, and promote cross-disciplinary collaborations in this area. Key goals include lowering barriers for analyzing large microscopy datasets, reducing the need for time-consuming reimplementation, and establishing an efficient data acquisition paradigm. The University of California, Santa Barbara will lead this 3-year research and development effort, with plans to create interconnected software packages for real-space and scattering-based analysis to enable integrating physics-based simulations and statistical machine learning. This work is expected to yield sustained impacts on both basic and applied sciences, ranging from biophysics to biomimetic materials.
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