ASME

Journal of Computing and Information Science in Engineering

Jami J. 
Shah, Ph.D.
Jami J. Shah

Areas of Interest

ADVANCED SIMULATION OF GD&T
APPLIED MACHINE LEARNING
CAD/CAM
CRASH WORTHINESS OF AUTOMOTIVE BODY STRUCTURES
TOPOLOGY OPTIMIZATION
Ohio State University, USA
Jami J. Shah is Honda Professor of Engineering Design and Director, Digital Design & Manufacturing Lab at The Ohio State University. He was previously Professor of Mechanical & Aerospace Engineering at Arizona State. Prior to his academic career, he worked in steel and chemical industry for 6 years, designing machinery and processing equipment. He is the co-author of 2 US patents, 2 books, and 300+ peer reviewed technical papers. He pioneered the development of Parametric & Feature based CAD/CAM Systems. His current research focus is on Applied Machine Learning, Topology Optimization, Crash Worthiness of Automotive Body Structures and Advanced Simulation of GD&T. He is the founding chief editor of ASME Transaction, the Journal of Computing & Information Science in Engineering (JCISE). He served as its Chief Editor from 2000 to 2010. He also design and managed the development of the first totally online journal review management system eLANE which was used not only by JCISE but several ASME and other journals. He currently serves as Co-Chief Editor of Journal of Computational Design & Engineering, Area Editor of Research in Engineering Design and Editorial Board of Experimental Results. He was elected Fellow of ASME in 2001. He is the recipient of numerous awards, including the 2008 ASME Design Automation award, Siemens PLM Engineering Education Excellence Award 2009 and the ASME CIE Lifetime Achievement Award given in 2012 and Boeing Performance Excellence Award. Lab website: https://mae.osu.edu/ddml

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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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