Azita Mayeli

Azita Mayeli is a Professor of Mathematics and Faculty Deputy Executive Officer at the City University of New York's Graduate Center. Her academic leadership and research contributions position her at the forefront of mathematical analysis within the CUNY system, with a focus on theoretical and applied harmonic analysis.

Her research spans abstract and classical harmonic analysis, Fourier analysis, representation theory, functional analysis, approximation theory, sampling theory, interpolation theory, and signal processing. She investigates fundamental connections between uncertainty principles, eigenvalue distributions, and signal recovery mechanisms across diverse mathematical structures including finite abelian groups, Riemannian manifolds, and fractal geometries. Her work bridges pure mathematics with practical applications in data science and signal processing.

Analysis of her 15 most recent publications (2022-2025) reveals a dominant focus on eigenvalue distributions of spatio-spectral operators, uncertainty principles in novel settings, and sparse signal recovery techniques. Her research demonstrates increasing interdisciplinary reach, connecting harmonic analysis with machine learning, geometric measure theory, and time-series analysis while maintaining deep theoretical foundations.

Scientific Awards:

• Feliks Gross Endowment Award (2015) - CUNY's highest honor for assistant professors, recognizing exceptional early-career contributions

While specific advising records and grant details are not documented in available sources, her extensive publication record and leadership position suggest active mentorship of graduate students and involvement in significant research funding. Her work on signal recovery mechanisms and mathematical foundations has clear implications for data science applications and computational mathematics.

Dr. Mayeli maintains active research laboratories focused on harmonic analysis applications, with particular emphasis on developing mathematical frameworks for signal processing in constrained environments. Her team explores connections between abstract function spaces and practical data recovery problems, contributing to both theoretical advances and algorithmic innovations.