ASME

Journal of Computing and Information Science in Engineering

Ying 
Liu, Ph.D.
Liu-Ying

Areas of Interest

BIG DATA AND ANALYTICS
CYBER MANUFACTURING
DATA-DRIVEN AND MACHINE LEARNING FOR ENGINEERING APPLICATIONS
ENGINEERING INFORMATICS
Cardiff University, UK
Ying Liu is currently a Reader in Intelligent Manufacturing and the Group Lead for High-value Manufacturing with the Institute of Mechanical and Manufacturing Engineering at the School of Engineering in Cardiff University, UK. Prior to that, he worked as an Assistant Professor with the Department of Mechanical Engineering at National University of Singapore (2010-2013) and with the Department of Industrial Systems and Engineering at the Hong Kong Polytechnic University (2006-2010). He obtained his Bachelors (1998) and Masters (2001) both from Mechanical Engineering at Chongqing University, China and PhD (2006) from the Innovation in Manufacturing Systems and Technology (IMST) program under the Singapore MIT Alliance (SMA) at the National University of Singapore. His research interests focus primarily on design informatics, design methodology and process, product design, manufacturing informatics, intelligent (smart) manufacturing, and advanced ICT in design and manufacturing, in which areas he has published over 120 scholarly articles, one edited book and more than seven special issues. He is an Associate Editor of ASME JCISE, IEEE T-ASE and the Journal of Industrial and Production Engineering (Taylor & Francis) and is on the Editorial Board of Advanced Engineering Informatics (Elsevier).

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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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