Johannes is the technical lead in the Automatic Differentiation (AD) product team at NAG since January 2021.
After his PhD at the RWTH Aachen University on "Hybrid Approaches to Adjoint Code Generation using dco/c++", he continued as a postdoctoral researcher in the broad field of AD. As the main developer of the software package dco/c++, Johannes has been working closely with clients from different areas (e.g., computational finance), both in industry and academia. Over more than the last 10 years, he successfully integrated and helped integrate AD solutions into large-scale numerical codes of various shapes and complexity. His main focus is on AD of C++, CUDA, and Fortran codes for CPU and GPU architectures.
With his expertise, Johannes is constantly working on innovation of AD techniques and products focusing on adding customer value, while he strengthens know-how and thought leadership in AD.