Dr. Martin Holler is a distinguished mathematician renowned for his foundational work in the mathematics of inverse problems and data science. Currently serving as Univ. Prof. Dr. at the University of Graz, he leads cutting-edge research at the intersection of mathematical theory and computational applications. After obtaining his PhD in Mathematics in 2013, he built his research career through prestigious postdoctoral positions at the University of Graz, École Polytechnique in Paris, and the University of Cambridge. His recognition as a Young Research Fellow at the Mathematics Münster Cluster of Excellence in 2021 highlights his growing influence in the mathematical sciences.
Dr. Holler is internationally acclaimed for his rigorous approaches to dynamic and multi-modality inverse problems and his seminal contributions to nonsmooth and model-based regularization methods. His research bridges variational methods, convex analysis, and the mathematics of machine learning to solve complex data-driven problems across scientific domains. With over 1,300 citations according to Google Scholar, his work has significantly shaped the theoretical foundations of mathematical image processing and biomedical imaging. His editorial leadership includes co-editing the Springer Handbook of Variational Methods for Nonlinear Geometric Data, establishing critical connections between mathematical theory and geometric data analysis. These methodological innovations have provided essential mathematical frameworks that underpin modern data processing techniques used across numerous scientific disciplines.
Beyond his technical contributions, Dr. Holler actively bridges mathematical theory with practical applications through collaborations spanning multiple disciplines including stochastics, geometry, and machine learning. His research has fostered interdisciplinary connections between traditionally separate fields, creating innovative pathways for mathematical approaches to data science challenges. His ongoing work explores the convergence of model-based approaches with machine learning techniques, pushing the boundaries of what mathematical methods can achieve in data analysis. Dr. Holler's future research directions promise to maintain his position at the forefront of mathematical innovation in the rapidly evolving field of data science.
