ASME

Journal of Computing and Information Science in Engineering

Yan 
Lu, Ph.D.
Yan Lu

Areas of Interest

ADDITIVE MANUFACTURING
DIGITAL TWIN
ENGINEERING INFOMATICS
MACHINE LEARNING
National Institute of Standards and Technology
Dr. Yan Lu is a member of the System Integration Division of the Engineering Lab at National Institute of Standards and Technology. She leads the Information Modeling and Testing group and is the project lead for the Data Integration and Management for Additive Manufacturing research at National Institute of Standards and Technology. Her research interest also includes smart manufacturing reference model and reference architecture, and service-oriented manufacturing. Dr. Yan Lu graduated from Tsinghua University with a BS and MS in automation control theory and from Carnegie Mellon University with a PhD in electrical and computer engineering. Before joining NIST, Dr. Lu was the head of Grid Automation and Production Operation and Optimization Research Group at Siemens Corporation, Corporate Technology. With Siemens, she has led and successfully delivered tens of million dollars of corporate funded, and government funded research projects in the areas of survivable control systems, energy automation and building energy management systems. She has published more than 140 peer reviewed journal and conference papers and was granted more than 15 patents in industry and building automation technology. Dr. Lu also worked for Seagate Research Center for two years on developing hard disk drive servo control. Dr. Yan Lu is a US expert for IEC TC 65, Co-Chair of ASTM F42.08 on AM Data, and a senior member of IEEE and a member of ASME. She is active in developing smart manufacturing and additive manufacturing standards.

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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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