Zev Woodstock

Zev Woodstock is an Assistant Professor in the Department of Mathematics & Statistics at James Madison University (JMU). His research focuses on nonsmooth optimization, splitting algorithms, machine learning, and monotone operator theory, with applications in data science, signal/image/audio processing, and large-scale optimization. He holds a PhD in Mathematics (2021) and MS in Applied Mathematics (2018) from North Carolina State University, and a BS in Mathematics (2016) from JMU. Previously, he was a postdoctoral researcher at the Interactive Optimization and Learning Lab at TU Berlin and Zuse Institute Berlin.

Education

• PhD in Mathematics, North Carolina State University, 2021
• MS in Applied Mathematics, North Carolina State University, 2018
• BS in Mathematics, James Madison University, 2016

Research Interests

Dr. Woodstock’s work spans optimization theory and applications, including convex/nonconvex optimization, fixed point algorithms, and signal recovery. He develops methods for solving nonlinear inverse problems using firmly nonexpansive operators, with contributions to variational inequality models, projection-based techniques, and block-iterative schemes. His applied work includes audio signal restoration, large-scale optimization, and machine learning infrastructure.

Key Research Trends

Recent publications emphasize efficient algorithms for linear minimization, asynchronous optimization frameworks, and robust signal recovery from inconsistent nonlinear data. His work bridges theoretical operator theory with practical applications in imaging, audio processing, and distributed computing.

Awards and Grants

Recipient of National Science Foundation Grants DGE-1746939 and CCF-1715671 for collaborative research in approximation theory and optimization.

Labs and Collaborations

Current affiliations include the Department of Mathematics & Statistics at JMU. Previously, he collaborated with the Interactive Optimization and Learning Lab at TU Berlin, focusing on convex optimization and algorithmic design.