Mark A Iwen

Mark A Iwen is an Associate Professor at Michigan State University, holding dual appointments in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE). His research focuses on computational harmonic analysis, mathematical data science, signal processing, and algorithms for analyzing high-dimensional datasets. He has contributed to advancements in sparse Fourier transforms, compressive sensing, and sublinear-time algorithms for large-scale data.

Key research themes include:

• Efficient algorithms for high-dimensional PDEs and spectral methods
• Phase retrieval and inverse problems in imaging
• Optimization of tensor decompositions and dimensionality reduction techniques
• Development of sparse approximation methods with theoretical guarantees

His recent work emphasizes applications in:

• Sublinear-time algorithms for function approximation
• Terminal embeddings for manifold data
• Fast JL embeddings with bi-Lipschitz properties
• Tensor completion and low-rank approximations

Notable contributions include:

• Development of Sparse Harmonic Transforms for functions of many variables
• Advancements in distributed SVD algorithms for large networks
• Empirical and theoretical analysis of phase retrieval techniques
• Efficient sparse FFT implementations (e.g., DMSFT, GFFT)

Iwen collaborates on open-source code projects, including sparse FFT libraries and phase retrieval tools. His work bridges mathematical theory with practical applications in engineering and computational science.