Michael B. Wakin - Research

Models and Algorithms for
Signal and Data Processing

Curriculum Vitae (pdf)

We are looking to recruit talented Ph.D. students with research interests in mathematical signal processing, data science, sensing systems, quantum information, and machine learning. Financial support may be available via research assistantships and/or departmental teaching assistantships. Prospective students are encouraged to email Prof. Wakin with the subject line "Wakin group".

On this page: Research Interests | Research Group | Overview | Topic Areas and Selected Publications | Additional Information & Resources

Research Interests

Research Group

Current students in my group:



Effective techniques for signal and data processing often rely on some sort of model that characterizes the expected behavior of the signals/data. In many cases, the model conveys a notion of constrained structure or conciseness: signals may be bandlimited or sparse in some transform domain, data sets may be organized into a low-rank matrix, and so on.

Our research group focuses on developing

Some of our work has focused on the topic area of Compressive Sensing, where sparse models are used to reconstruct signals from small numbers of random linear measurements. More recently, however, many of the same core ideas (low-complexity models and optimization algorithms that exploit these models) have demonstrated their potential in a broad variety of new applications: medical imaging, radar imaging, super-resolution, dynamical systems analysis, low-rank matrix completion, machine learning, and so on. Our work aims to expand the relevance and understanding of low-complexity models and algorithms in these and other contexts.

Topic Areas and Selected Publications

A list of selected publications is below. A complete list of publications is available here.

Compressive Sensing

Machine Learning

Application Areas


Additional Information & Resources

Email: mwakin (at) mines (dot) e d u