Ronny Bergmann

Ronny Bergmann is an Associate Professor (Førsteamanuensis) at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, where he has been employed since March 2021. Previously, he served as a postdoctoral researcher and deputy head of the Numerical Analysis of Partial Differential Equations group at Chemnitz University of Technology, Germany, and earlier worked within the Image Processing Group at Kaiserslautern University of Technology where he completed his habilitation in early 2018. His doctoral research was conducted at the University of Lübeck under supervision of Jürgen Prestin.

Bergmann's research primarily focuses on nonsmooth optimization on manifolds, with particular emphasis on proximal methods and their applications in manifold-valued image processing. His work extends to optimization methods on manifolds and high-dimensional power manifolds, graph-based approaches for manifold-valued data processing that combine local and nonlocal methods, and anisotropic periodic translation invariant spaces with Fourier-based algorithms. His research bridges theoretical mathematics with practical computational applications in image processing and data analysis.

His recent publications demonstrate significant contributions to manifold optimization theory and applications, with a strong emphasis on developing computationally efficient algorithms for manifold-valued data. The publications span topics including Riemannian proximal gradient methods, Newton's methods on manifolds, total variation techniques for mesh processing, and geometric approaches to Kalman filtering. His work often involves developing practical implementations, such as the Manopt.jl and Manifolds.jl packages in Julia, which have demonstrated substantial performance improvements over existing tools.

Bergmann's research has been published in top-tier journals including SIAM Journal on Scientific Computing, Journal of Optimization Theory and Applications, and SIAM Journal on Optimization, reflecting the high quality and impact of his contributions to the field of manifold optimization and its applications.

As both a theoretical mathematician and computational scientist, Bergmann has made significant contributions to advancing the state-of-the-art in optimization on manifolds, particularly in developing methods that maintain intrinsic geometric properties while achieving computational efficiency. His work has applications across multiple domains including image processing, computer graphics, robotics, and data science.