Lane Enrique Schultz

Materials Scientist & Engineer

Specializing in machine learning for materials, scientific computing, and metallic glasses

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About Me

Lane Schultz

Hello, I'm an accomplished materials scientist and engineer with an impressive academic background, including a Ph.D. and M.S. in Materials Science and Engineering from the University of Wisconsin-Madison and a B.S. in Engineering from Fort Lewis College, all with excellent GPAs. My technical skills span a wide range of programming languages, software tools, and advanced materials characterization and simulation techniques. Through my research experience, I have made significant contributions in the fields of machine learning for materials, scientific computing, and metallic glasses. I have authored or co-authored numerous peer-reviewed journal publications.

Education

Ph.D. in Materials Science and Engineering

University of Wisconsin-Madison

Distributed Minor in Machine Learning

M.S. in Materials Science and Engineering

University of Wisconsin-Madison

B.S. in Engineering

Fort Lewis College

Concentration in Electro/Mechanical Engineering

Minor in Mathematics

Research

Machine Learning for Materials

Machine Learning for Materials

Developing and applying machine learning algorithms to predict material properties and accelerate materials discovery.

Metallic Glasses

Metallic Glasses

Investigating the structure, properties, and applications of metallic glasses through computational and experimental methods.

Scientific Computing

Scientific Computing

Developing computational tools and simulations for materials science applications, with a focus on high-performance computing.

Selected Publications

Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization

Schultz, L.E., Afflerbach, B., Voyles, P.M., Morgan, D.

10.1016/j.jmat.2024.100964

Journal of Materiomics, 2023

A study on predicting glass-forming ability in metallic alloys using various machine learning and modeling approaches.

Determining Domain of Machine Learning Models using Kernel Density Estimates: Applications in Materials Property Prediction

Schultz, L.E., Wang, Y., Jacobs, R., Morgan, D.

10.48550/arXiv.2406.05143

arXiv, 2025

A kernel density estimation approach to assess a machine learning model's domain of applicability.

Documents

Curriculum Vitae

NCEES FE

Academic Transcript

Ph.D. Diploma

Get in Touch

Albuquerque, New Mexico

laneenriqueschultz@gmail.com

(806) 678-6904