Michael Schneier

Michael Schneier is an adjunct professor in the Department of Mathematics at the University of Pittsburgh. He holds a Ph.D. in Scientific Computing from Florida State University (2018), advised by Professor Max Gunzburger. His research focuses on computational mathematics, uncertainty quantification, machine learning, reduced order modeling, and computational fluid dynamics, with a particular emphasis on leveraging data to enhance traditional computational fluid dynamics algorithms.

Research interests include advancing numerical methods for partial differential equations, turbulence modeling, and the integration of machine learning techniques into fluid dynamics simulations. His work emphasizes improving algorithmic efficiency and accuracy through innovative approaches like reduced-order models and latent diffusion frameworks.

Selected publications explore topics such as latent neural PDE solvers, turbulence diagnostics, and ensemble-based numerical methods for fluid flows. His contributions span both theoretical advancements and practical applications in computational science.

No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research may involve collaborative projects. His work is centered at the Department of Mathematics within the Dietrich School of Arts and Sciences.