ASME

Journal of Computing and Information Science in Engineering

Jianxi 
Luo, Ph.D.
Jianxi Luo

Areas of Interest

DATA-DRIVEN INNOVATION
Singapore University of Technology and Design, Singapore
Jianxi Luo holds a Ph.D. in Engineering Systems and S.M. in Technology and Policy from Massachusetts Institute of Technology, and M.S. and B.E. in Mechanical Engineering from Tsinghua University. He is currently a tenured Associate Professor in the Engineering Product Development Pillar and the Design and Artificial Intelligence Cluster at Singapore University of Technology and Design (SUTD). He also directs the Data-Driven Innovation Lab at SUTD. He serves as Managing Editor of the Journal of Engineering Design, Associate Editor of Design Science, Associate Editor of AI EDAM, Associate Editor of ASME JCISE, Department Editor of IEEE Trans on Engineering Management, editorial board member of Research in Engineering Design, and executive committee member of the Council of Engineering Systems Universities (CESUN). He previously served as Chair of the INFORMS TIME Section. His research focuses on Data-Driven Innovation (DDI) in engineering design, developing DDI ontology, theories, methods, and tools. Stanford and Elsevier have recognized him among the top 2% scientists globally in the field of Design Practice & Management since 2019. His work received over 20 research or design awards from ASME, INFORMS, Design Society, Complex System Society, etc., including Best Paper Award from ASME Design Theory and Methodology Conference in 2019 and Editor’s Choice Paper Award from ASME Journal of Mechanical Design in 2020.

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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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