Lori Graham-Brady

About

Lori Graham-Brady is a professor of civil and systems engineering at Johns Hopkins University and serves as the vice dean for faculty in the Whiting School of Engineering. Her research focuses on stochastic finite element methods, probabilistic mechanics, stochastic simulation of material properties, and micromechanics. She is the current director of the JHU Center on AI for Materials in Extreme Environments (CAIMEE) and has previously directed the Center for Materials in Extreme Dynamic Environments, a collaborative program with the Army Research Labs aimed at multiscale modeling and design of materials for extreme dynamic environments, particularly ceramics and composites for armor applications. Graham-Brady was also the founding director of the Center on High-throughput Materials for Extremes (HT-MAX) and the founding associate director of the Hopkins Extreme Materials Institute (HEMI). Her work provides a fundamental understanding of the connections between material-scale uncertainties and the performance and reliability of structures, leveraging machine learning tools to solve complex mechanics problems. She led the development of an AI-focused facility for materials design, emphasizing high-strain and temperature-rate environments. Her contributions to engineering education include a multi-year NSF-funded program on modeling complex systems and developing the scientific basis for multiscale multiphysics models, with a focus on diversity and inclusion. Graham-Brady has…

Selected publications

• Materials for extreme environments

  Nature Reviews Materials · 2022 · 258 citations

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• Stress field prediction in fiber-reinforced composite materials using a deep learning approach

  Composites Part B Engineering · 2022-04-22 · 219 citations

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Bayesian neural networks for uncertainty quantification in data-driven materials modeling

  Computer Methods in Applied Mechanics and Engineering · 2021 · 170 citations

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• Accelerated multiscale mechanics modeling in a deep learning framework

  Mechanics of Materials · 2023-06-07 · 59 citations

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• Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network

  Computer Methods in Applied Mechanics and Engineering · 2024-02-02 · 45 citations

  articleSenior authorCorresponding
  PublisherDOI

Awards & honors

• Presidential Early Career Awards for Scientists and Engineer…
• International Association for Structural Safety and Reliabil…
• Walter L. Huber Civil Engineering Research Prize
• William H. Huggins Award for Excellence in Teaching
• Fellow of ASCE EMI

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