Codesign of parsimonious machine learning for high velocity materials microscopy on the edge

Materials in Extreme Environments: New Monitoring Tools and Data-Driven Approaches

Presented by the National Academies’ Condensed Matter and Materials Research Committee Under the auspices of the Board on Physics and Astronomy

October 6, 2022

Practical Deep Learning in Scanning Probe Microscopy

Fall MRS 2020

Automatic Feature Extraction from Hyperspectral Imagery using Deep Recurrent Neural Networks

Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics

Deducing Inference from Hyperspectral Imaging of Materials

Physics in Machine Learning Workshop May 29, 2019