ASME

Journal of Computing and Information Science in Engineering

Linkan 
Bian, Ph.D.
Linkan Bian (photo by Megan Bean / © Mississippi State University)

Areas of Interest

ADDITIVE MANUFACTURING
DIGITAL TWIN
ENGINEERING INFORMATICS
MACHINE LEARNING
Mississippi State University
Dr. Linkan Bian is a Thomas B. & Terri L. Nusz Endowed Professor in Industrial and Systems Engineering Department at Mississippi State University. He received his Ph.D. in Industrial and Systems Engineering from Georgia Institute of Technology, and B.S. in Applied Mathematics from Beijing University. Dr. Bian’s research interests focus on the analytics of Big Data generated from complex engineering systems. Methodology of his research includes machine learning, surrogate modeling, and uncertainty quantification. His research has been applied to areas including additive manufacturing, predictive maintenance, cybersecurity, and other engineering systems. Dr. Bian has published one book and over 60 peer-reviewed papers that appear in prestigious journals. His research has received federal funding from NSF, NIH, DoD, DoE, and industrial companies. Dr. Bian received the Outstanding Young Investigator Award from Institute of Industrial and Systems Engineering (IISE), as well as multiple Best Paper Awards. He was the president for IISE Quality Control and Reliability Engineering division (2020-2022).

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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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