Tyler Maunu

Tyler Maunu is an Assistant Professor of Mathematics at Brandeis University, affiliated with the Department of Mathematics and the Benjamin and Mae Volen National Center for Complex Systems. His research focuses on statistical methodologies, optimization algorithms, machine learning, and their applications to computer vision and data science. He holds a Ph.D. in Mathematics from the University of Minnesota-Twin Cities, alongside multiple advanced degrees from the same institution.

Maunu's research advances robust subspace recovery and optimal transport, emphasizing scalable and privacy-aware techniques. His work bridges theoretical foundations (e.g., non-convex optimization landscapes) with practical applications in data recovery and generative modeling. Notable areas include Bures-Wasserstein geometry, stochastic gradient methods, and adversarial robustness in high-dimensional data analysis.

His articles span topics like preconditioned Langevin Monte Carlo, optimal transport barycenters, and scalable graph matching algorithms. While no explicit awards are cited, his contributions reflect ongoing innovation in computational statistics and optimization. His affiliation with the Volen Center underscores interdisciplinary engagements in complex systems research.