ASME

Journal of Computing and Information Science in Engineering

Yan 
Wang, Ph.D.
Yan Wang

Areas of Interest

CYBER-PHYSICAL SYSTEMS
DATA ANALYTICS
MULTISCALE CAD/CAM/CAE
UNCERTAINTY QUANTIFICATION
Georgia Institute of Technology, USA
Yan Wang is a Professor of Mechanical Engineering and leads the Multiscale Systems Engineering Research Group at the Georgia Institute of Technology. His research areas include computer-aided design (CAD), computer-aided manufacturing, multiscale modeling and simulation, materials design, uncertainty quantification, and physics- informed machine learning. He has published over 100 archived journal papers and over 100 peer-reviewed conference papers, including the ones with best conference paper awards at the American Society of Mechanical Engineers (ASME) Computers & Information in Engineering (CIE) Conference, ASME Multibody Systems, Nonlinear Dynamics, and Control Conference, The Minerals, Metals & Materials Society (TMS) World Congress on Integrated Computational Materials Engineering, the Institute of Industrial & Systems Engineers (IISE) Industrial Engineering Research Conference, and the International CAD Conference. He is a recipient of the U.S. National Science Foundation (NSF) CAREER Award, a National Aeronautics and Space Administration (NASA) Faculty Fellow, and an ASME Fellow. He currently serves on the ASME leadership teams of Digitalization and Intelligent Manufacturing Technology Groups, was the Chair of ASME CIE Division and the Chair of ASME Advanced Modeling & Simulation Technical Committee, and is regularly invited to review proposals for NSF, NASA, Natural Sciences and Engineering Research Council of Canada, European Research Council, German Research Foundation, Singapore Agency for Science, Technology and Research (A*STAR) Biomedical Research Council, and Hong Kong Nano and Advanced Materials Institute. He received his B.S. degree from Tsinghua University, M.S. from Chinese Academy of Sciences, and Ph.D. from the University of Pittsburgh.

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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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