Journal of Computing and Information Science in Engineering

Mani, Ph.D.
Mahesh Mani

Areas of Interest

National Institute of Standards and Technology, USA
Dr. Mahesh Mani is a Technical Program Manager at the National Institute of Standards and Technology (NIST) Office of Advanced Manufacturing. Prior to this position, Dr. Mani was a Technology Manager with the US Department of Energy, Washington DC. Dr. Mani’s primary research interests include smart and sustainable manufacturing, additive manufacturing, modeling and simulation for sustainability, distributed and collaborative manufacturing including reconfigurable systems, manufacturing information networks and interoperable solutions. Dr. Mani has authored or co-authored more than 60 articles in journals, conference proceedings, technical reports and book chapters. Dr. Mani currently serves as an Associate Editor for ASME Journal of Computing and Information Science in Engineering (JCISE). Within ASME Computers and Information in Engineering (CIE) division, Dr. Mani has served in various leadership roles and is currently a member of the Executive Committee. In 2016 Dr. Mani received the ASME CIE Young Investigator Award for his potential to advance computers and information in Engineering. Dr. Mani was earlier awarded the 2012 CIE Service Award in recognition of his sustained and outstanding contributions to the ASME CIE Division’s Newsletter. He received ASME best paper awards in 2015 and 2016. Since 2017, Dr. Mani is a member of the Washington Academy of Sciences (WAS) and American Association for the Advancement of Science (AAAS). Dr. Mani was awarded the 2022 ASME CIE Distinguished Service Award in recognition of distinguished and outstanding service for the computers and information in Engineering Division. Dr. Mani received his PhD in Mechanical Engineering from the National University of Singapore.

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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.


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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