This $250,000 award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports a collaborative research project at The Ohio State University to develop scalable, robust, and distributed optimization approaches for recovering low-dimensional structures from incomplete tensor data. The project aims to overcome computational challenges in analyzing large-scale tensor data by directly optimizing over low-dimensional tensor representations. Key research activities include studying stable embeddings of low-dimensional tensors, characterizing the geometric landscape of factorized nonconvex problems, and enhancing optimization algorithms with distributed techniques. The project has the potential to advance the field of tensor analysis and yield benefits across diverse domains such as signal processing, biomedical imaging, machine learning, and quantum information science. The award period runs from September 1, 2024 to August 31, 2027.
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