Alex Landauer (he/him; ORCID: 0000-0003-2863-039X) is an experimental solid mechanician in the Security Technologies Group. Alex specializes in full-field measurements in mechanics across domains including soft materials, impact protection, 3D printing, semiconductors and cell mechanobiology. Methods include commercial off-the-shelf and custom instruments such as universal testing systems, dynamic mechanical analysis, drop tower impact, and optical imaging systems. Full-field measurement strategies focus on 2D, surface-3D and volumetric digital image correlation (DIC/DVC), particle image velocimetry and single particle tracking (PIV and PTV/SPT), in addition to other image analysis modalities. These enable advanced technique such as light field microscopy (LFM) and traction force microscopy (TFM) measurements, computational methods including finite element model updating (FEMU) and constitutive model fitting routines, and provide key data and diagnostics for material characterization.
Overall, Alex's experimental and computational capabilities build toward an experimental mechanics program that incorporates sophisticated force application and imaging, deformation reconstruction, inverse property identification, machine learning (ML) and constitutive modeling techniques for material measurement and characterization. His interests involve developing experimental systems and techniques to explore and model materials, structure-property relationships, and understand uncertainty and error sources. Alex's collaborative approach and commitment to open software and data strives to help material and physical scientists, biomedical engineers, materials manufacturers, and protective equipment developers understand and design highly performant systems.
Warning: Alex's twin brother Orion Kafka is also a research engineer in MML at the Boulder site and confusion is possible.
(Principal NIST collaborators are mentioned parenthetically.)
Landauer A.K. "Dataset Generation for Digital Image/Volume Correlation and Particle Tracking: Applications to Machine Learning-Based Techniques", Society of Engineering Science Annual Conference. Oct, 2023.