معرفی
Dr. Mahsa Taheri is a Scientific Associate (faculty-level researcher) in the Department of Mathematics at the University of Hamburg, embedded within the Faculty of Mathematics, Computer Science and Natural Sciences. She belongs to the research section Mathematical Statistics and Stochastic Processes and focuses on the emerging area Mathematics of data-based methods.
Research Interests: Her work sits at the intersection of mathematical statistics, stochastic processes, and machine-learning theory. She develops non-asymptotic guarantees for modern learning algorithms, studies regularization and sparsity in deep neural networks, and quantifies statistical-computational trade-offs in high-dimensional regimes.
Publication Trends: Across 13 peer-reviewed contributions (2016-2025) she consistently addresses the theoretical foundations of learning algorithms, with a marked emphasis on sample complexity, regularization, sparsity, and efficiency in neural networks and high-dimensional regression. The 2025 preprints extend this agenda to diffusion models and tail-index estimation.
Scientific Awards: None explicitly reported in the available material.
Advising & Grants: No advisee list or grant information was provided in the source text.
Labs & Teams: She is part of the Mathematics of data-based methods research team within the Mathematical Statistics and Stochastic Processes unit; no dedicated laboratory name is mentioned.
