Mauro Maggioni

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University, holding the Bloomberg Distinguished Professorship. His research bridges theoretical mathematics with data science applications across physical and biological systems.

His educational background includes:

• B.S. in Mathematics from Universitá degli Studi in Milan, Italy
• Ph.D. in Mathematics from Washington University in St. Louis

Dr. Maggioni develops mathematical frameworks for high-dimensional data analysis using multiscale techniques from Harmonic Analysis and Approximation Theory. His work enables scalable algorithms for molecular dynamics simulation, agent-based system modeling, hyperspectral imaging, and reinforcement learning, emphasizing interpretability and generalizability in scientific discovery.

His major scientific awards include:

• Popov Prize in Approximation Theory (2007)
• NSF CAREER Award (2008)
• Sloan Fellowship (2008)
• Fellow of the American Mathematical Society (2013)

He mentors a diverse group of researchers including Dan Popescu, Jason Miller, Patrick-Martin, Jin Zhou, Christian Kummerle, Wenjing Liao, Stas Minsker, Nate Strawn, Mark Iwen, James M. Murphy, Anna V. Little, and Jason D. Lee. His research is supported by the National Science Foundation and Simons Foundation, including a 2020-2021 Simons Fellowship during sabbatical.

His Johns Hopkins research group actively recruits graduate students and postdocs for projects in stochastic dynamical systems, statistical signal processing, and machine learning, maintaining strong collaborations with institutions like the Flatiron Institute.