معرفی
Sebastian Krämer is a researcher at the Institute for Geometry and Practical Mathematics at RWTH Aachen University, working under the supervision of Prof. Markus Bachmayr and Prof. Lars Grasedyck. His research focuses on tensor networks, low-rank approximations, and numerical methods for high-dimensional problems. He has made significant contributions to the field of tensor train formats and rank minimization techniques.
Dr. Krämer's research interests span tensor networks, low-rank approximations, numerical linear algebra, high-dimensional approximation, tensor train formats, and machine learning optimization. His work centers on developing efficient algorithms for tensor decompositions, particularly focusing on alternating least squares methods, iteratively reweighted least squares approaches, and geometric constraints for tensor singular values. His research bridges theoretical numerical analysis with practical applications in high-dimensional data processing and scientific computing.
His publication record shows a consistent output of high-quality work in top numerical analysis journals, with recent publications in 2024 demonstrating ongoing active research. His work demonstrates expertise in both theoretical aspects of tensor decompositions and practical implementation of numerical algorithms. He has developed several open-source toolboxes for tensor network arithmetic and tensor train feasibility problems, which have been widely used by the research community.
Dr. Krämer has been actively involved in teaching, serving as a lecturer and assistant for various mathematics courses at RWTH Aachen, including Numerical Mathematics for mathematicians and civil engineers. He has also contributed to specialized research schools on high-dimensional approximation and deep learning, developing course materials and providing instruction. His professional activities include regular participation in the GAMM conference since 2017 and peer review activities for SIAM journals since 2015.
