Journal of Computing and Information Science in Engineering

Bernstein, Ph.D.
William Bernstein

Areas of Interest

Air Force Research Laboratory (AFRL)
Dr. William Z. Bernstein is the Branch Technical Advisor of the Digital Manufacturing and Supply Chain Branch in the Manufacturing and Industrial Technologies Division of the Materials and Manufacturing Directorate at the Air Force Research Laboratory (AFRL). He leads the Advanced Manufacturing Technologies (AMT) ManTech product portfolio, which comprises of the Additive Manufacturing, Digital Enterprise, and Future Factory programs. He is also a part of the RX Digital Manufacturing Research Team, which internally addresses challenges faced by the Air Force and Space Force services. His research interests are information modeling, system interoperability, and human-centered interfaces. He works on applications related to Computer-Aided Design and Manufacturing, Industrial Augmented Reality, Digital Manufacturing, and Sustainable Design and Manufacturing. He has published more than sixty technical articles in journals and refereed conference proceedings and has been awarded two US patents. He serves on the American Society of Mechanical Engineers (ASME) Design for Manufacturing and the Life Cycle (DFMLC) Technical Committee and on the Joint Defense Manufacturing Technology Panel (JDMTP) Advanced Manufacturing Enterprise (AME) Subpanel. He serves as an Associate Editor of the ASME Journal of Computing and Information Science in Engineering. He has received multiple honors and awards. Representative examples include the 2019 ASME Computers in Engineering (CIE) Young Engineer Award and a 2020 Department of Commerce (DOC) Bronze Medal Award for his contributions to ASTM Sustainable Manufacturing (E60) standards.

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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.


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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