ASME

Journal of Computing and Information Science in Engineering

Johann 
Guilleminot, Ph.D.
Civil and Environmental Engineering staff and faculty headshots

Areas of Interest

DATA-DRIVEN ENGINEERING APPLICATIONS
MACHINE LEARNING FOR ENGINEERING APPLICATIONS
NUMERICAL METHODS DEVELOPMENT FOR MODELING
UNCERTAINTY QUANTIFICATION
Duke University
Johann Guilleminot joined Duke on July 1, 2017, as an Associate Professor of Civil and Environmental Engineering. Prior to that, he held a Maître de Conférences position in the Multiscale Modeling and Simulation Laboratory at Université Paris-Est in France. He earned an MS (2005) and PhD (2008) in Theoretical Mechanics from the University of Lille 1 Science and Technology (France), and received his Habilitation (2014) in Mechanics from Université Paris-Est. Habilitation is the highest academic degree in France. Dr. Guilleminot’s research focuses on uncertainty quantification, computational mechanics and materials science, as well as on topics at the interface between these fields. He is particularly interested in the multiscale analysis of linear/nonlinear heterogeneous materials (including biological and engineered ones), homogenization theory, statistical inverse problems and stochastic modeling with applications for computational science and engineering. He received various awards, including the French Early Career Award (2012) and the NSF CAREER Award (2020).

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Announcements

Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing

Geometric information, such as three-dimensional (3D) shapes and network topologies, has been increasingly explored in manufacturing research. For example, characterizing geometric information in 3D-printed parts, in-situ or ex-situ, opens opportunities for defect detection, quality improvement, and product customization. However, geometric data mining remains critically challenging. Geometric information is embedded in complex data structures, such as 3D point clouds, graphs, meshes, voxels, high-dimensional images, and tensors, which possess challenges for analysis due to their high-dimensionality, high-volume, unstructured, multimodality characteristics. Additional challenges stem from compromised data quality (e.g., noisy and incomplete data), the need for registration, etc.

Announcements

2023 Reviewer’s Recognition

The Editor and Editorial Board of the Journal of Computing and Information Science in Engineering would like to thank all of the reviewers for volunteering their expertise and time reviewing manuscripts in 2023. Serving as reviewers for the journal is a critical service necessary to maintain the quality of our publication and to provide the authors with a valuable peer review of their work.

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