ASME

Journal of Computing and Information Science in Engineering

Charlie C.L. 
Wang, Ph.D.
CCLWang

Areas of Interest

ADVANCED MANUFACTURING
COMPUTER-AIDED DESIGN
GEOMETRIC COMPUTING
ROBOTICS
The University of Manchester, UK
Charlie C. L. Wang is currently a Professor of Mechanical and Automation Engineering and Director of Precision Engineering Institute at the Chinese University of Hong Kong (CUHK). Before being re-appointed back to CUHK in July 2018, he was a tenured Professor and Chair of Advanced Manufacturing (2016-2018) at Delft University of Technology (TU Delft), The Netherlands and Professor (2015-2016) / Associate Professor (2009-2015) / Assistant Professor (2003-2009) of Mechanical and Automation Engineering at CUHK. He holds a non-paid position as Professor of Advanced Manufacturing at TU Delft (2018-2023) at present, and was a visiting professor at University of Southern California (2011). Prof. Wang received a few awards from professional societies including the ASME CIE Excellence in Research Award (2016), the ASME CIE Young Engineer Award (2009), the Best Paper Awards of ASME CIE Conferences (twice in 2008 and 2001 respectively), the Prakash Krishnaswami CAPPD Best Paper Award of ASME CIE Conference (2011), and the NAMRI/SME Outstanding Paper Award (2013). He received his B.Eng. degree (1998) in mechatronics engineering from Huazhong University of Science and Technology and his M.Phil (2000) and Ph.D. (2002) degrees in mechanical engineering from Hong Kong University of Science and Technology (HKUST). He is a Fellow of ASME with expertise in geometric computing, design and manufacturing.

Return to the Editorial Board

More To Explore

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.

LATEST PAPERS

Latest Papers from ASME's Digital Collection