Bio
Steffen Schotthoefer is the current Householder Fellow in the Mathematics in Computation Section at Oak Ridge National Laboratory, affiliated with the Multiscale Methods and Dynamics Group.
Steffen's work centers on creating efficient numerical methods for training and fine-tuning AI models in environments with limited resources and at large scales. He investigates low-rank methods for model compression to minimize the computational cost of neural network training and inference.
In addition, Steffen develops neural network-based surrogate models for scientific domains such as radiation transport and plasma dynamics. His research aims to tackle the challenges posed by memory and communication bottlenecks in large-scale simulations.
Prior to joining ORNL, Steffen completed his PhD in Applied Mathematics at KIT, Germany, focusing on neural network-based surrogate modeling for radiation transport. During his doctoral studies, he devised numerical methods for the simulation of kinetic PDEs and neural network training, establishing the foundation for his current research.
Education
- Karlsruhe Institute of Technology, Germany, Ph.D. Mathematics, 2023
- TU Kaiserslautern, Germany, M.Sc. Mathematics and Computer Science, 2020
- TU Kaiserslautern, Germany, B.Sc. Mathematics, 2017
Professional Service
- Reviewer for NeurIPS, ICML, SIAM
- Organizer of the International Workshop on Moment Methods in Kinetic Theory IV
Professional Affiliations
Other Publications
See Google Scholar