Michael A. Perlmutter

Michael A. Perlmutter is an Assistant Professor in the Department of Mathematics at Boise State University and an affiliate faculty member in the Computing Ph.D. program. His research bridges theoretical and applied aspects of machine learning, data science, and inverse problems, with interdisciplinary applications in biomedicine and environmental systems.

Research Interests: His work primarily focuses on Geometric Deep Learning, particularly neural network models for non-Euclidean data such as graphs and manifolds. This includes theoretical development of the geometric scattering transform and designing deep learning architectures for signed and directed graphs. He also investigates Phase Retrieval in ptychographic imaging, emphasizing robust, efficient algorithms with provable guarantees. His applied interests span AI-aided drug discovery, analysis of metabolic networks, single-cell data prediction, tensor compression, audio denoising, and data science for ecological and hydrological systems.

Scientific Awards:

Advising and Grants: While specific students and grants are not listed, his active research program and Ph.D. affiliation suggest involvement in mentoring graduate students and pursuit of external funding, particularly in interdisciplinary data science and machine learning. His career trajectory includes multiple competitive postdoctoral appointments, indicating a strong research profile.

Labs and Teams: Though not explicitly named, his work implies collaboration with interdisciplinary teams in computational mathematics, biomedical data science, and environmental modeling, possibly within or affiliated with Boise State's data science and computing initiatives.