ASME

Journal of Computing and Information Science in Engineering

Gregory  
Vogl
VoglPS

Areas of Interest

Augmented Intelligence for Manufacturing Systems
Manufacturing Systems
National Institute of Standards and Technology
Gregory W. Vogl received the Bachelor’s degree in Engineering Science and Mechanics and Master’s degree and Ph.D. in Engineering Mechanics from Virginia Polytechnic Institute and State University of Blacksburg, Virginia, USA in 2000, 2003, and 2006, respectively. After a National Research Council postdoctoral fellowship at the National Institute of Standards and Technology (NIST), he joined the Production Systems Group at NIST and worked on machine tool standards and vibration metrology. Currently, Dr. Vogl leads the Augmented Intelligence for Manufacturing Systems (AIMS) project, which seeks to develop augmented intelligent solutions for manufacturing systems. Dr. Vogl was elected in 2023 as an Associate Member of the International Academy for Production Engineering (CIRP) and is the recipient of a NIST Engineering Laboratory Mentoring Award, NIST Colleagues’ Choice Award, and NIST Engineering Laboratory Outstanding Publication Award.

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Announcements

July 17 Spotlight: “Information Embedding in Additively Manufactured Parts Through Printing Speed Control” 

A recording is now available for the July 17, 2024 JCISE Spotlight talk by Professor Jitesh Panchal on paper co-authored with Karim A. ElSayed entitled “Information Embedding in Additively Manufactured Parts Through Printing Speed Control” J. Comput. Inf. Sci. Eng. J. Comput. Inf. Sci. Eng. Jul 2024, 24(7): 071005 (10 pages) Paper No: JCISE-23-1496 https://doi.org/10.1115/1.4065089.

Announcements

June 18, 2024 Spotlight: “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset”

A recording is now available on Youtube for the June 18, 2024 Spotlight talk by Professor Christian Soize (Université Gustave Eiffel) on his paper “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset,”

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