Summary
Lane Schultz is a postdoctoral researcher at Sandia National Laboratories with nine years of experience applying Python-driven workflow automation, atomistic modeling, and machine learning to predict materials properties. He earned his PhD and MS in Materials Science and Engineering from the University of Wisconsin–Madison, where he developed domain-of-applicability methods for ML models, built high-throughput workflows for interatomic potentials, and helped design and administer HPC clusters. Lane blends hands-on software engineering (PEP8-compliant scientific tools and environment modules) with deep physical insight into metallic glass formation, enabling more reliable uncertainty estimates and model selection. Comfortable moving between code, cluster administration, and simulation, he brings a reproducible, production-minded approach to materials modeling and a track record of turning complex physics into automatable pipelines.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Materials Science and Engineering, Doctor of Philosophy - PhD Materials Science and Engineering at University of Wisconsin-Madison
Bachelor's degree Engineering, Bachelor's degree Engineering at Fort Lewis College
High School Diploma, High School Diploma at Menard High School
High School Diploma, High School Diploma at Manzano High School