Stefanie Günther
About Stefanie Günther
Stefanie Günther is a Postdoctoral Researcher specializing in deep layer-parallel training and optimization techniques in machine learning. She has extensive experience in high-performance computing and has worked at several prestigious institutions, including RWTH Aachen University and Lawrence Livermore National Laboratory.
Work at Lawrence Livermore National Laboratory
Stefanie Günther has been employed as a Postdoctoral Researcher at Lawrence Livermore National Laboratory since 2019. In this role, she focuses on enhancing computational efficiency through high-performance computing (HPC). Her work involves deep layer-parallel training, which accelerates the training of deep neural networks by parallelizing computations. Additionally, she engages in parallel-in-time integration and optimization, methods that improve the efficiency of solving time-dependent problems.
Education and Expertise
Stefanie Günther holds a Dipl.-Math. in Applied Mathematics from Universität Trier, where she studied from 2006 to 2012. She furthered her education at RWTH Aachen University, earning a Dr. rer. nat. in Computational and Applied Mathematics between 2012 and 2017. Her expertise includes adjoint sensitivity analysis, which assesses how variations in input parameters influence outputs in mathematical models, and optimization techniques for unsteady partial differential equations.
Background in Research Positions
Prior to her current position, Stefanie Günther worked in various research roles. She served as a Research Scientist at the University of Kaiserslautern from 2014 to 2019. Earlier, she was a Research Scientist at RWTH Aachen University from 2012 to 2014. She also held visiting researcher positions at prestigious institutions, including the Massachusetts Institute of Technology in 2013, Argonne National Laboratory in 2016, and Lawrence Livermore National Laboratory in 2018.
Internship Experience
Stefanie Günther gained practical experience as an Internship Student at the German Aerospace Center (DLR) in 2010, where she worked for two months in the Braunschweig Area, Germany. This internship contributed to her foundational knowledge in applied mathematics and high-performance computing, which she has continued to develop throughout her academic and research career.