ASME

Journal of Computing and Information Science in Engineering

Vinayak Raman 
Krishnamurthy, Ph.D.
Vinayak Krishnamurthy

Areas of Interest

ARTIFICIAL INTELLIGENCE
COLLABORATIVE DESIGN
COMPUTATIONAL FOUNDATIONS FOR ADDITIVE MANUFACTURING
COMPUTATIONAL GEOMETRY
COMPUTER AIDED DESIGN
COMPUTER-AIDED MANUFACTURING
CONCEPTUAL DESIGN
GEOMETRIC REASONING
HUMAN COMPUTER INTERACTIONS
VIRTUAL AND AUGMENTED REALITY
Texas A&M University, USA
Vinayak Krishnamurthy is an Assistant Professor and Morris E. Foster Fellow II in the J. Mike Walker’66 Department of Mechanical Engineering and Department of Computer Science and Engineering (by affiliation) at Texas A&M University. He directs the Mixed-Initiative Design Lab (MIDL) at Texas A&M University. His research is positioned at the intersection of computer-aided product design, human-computer interaction, and artificial intelligence. His research seeks to make fundamental contributions to the disciplinary areas of geometric & topological computing, virtual/augmented/mixed reality, and intelligent user interfaces for design. He applies the knowledge gained in these areas to various domains such as metamaterial design, computational fabrication, data-driven design, collaborative design, autonomous systems, surgical training, and engineering education. Dr. Krishnamurthy is the recipient of the 2021 ASME CIE Young Engineer Award, the 2021 NSF CAREER Award, and the 2018 Peggy L. and Charles L. Brittan Teaching Award for Outstanding Undergraduate Teaching. His dissertation research led to the commercial deployment of zPots, a virtual pottery app using Leap Motion controller. Through the NSF-AIR program, we collaborated with zeroUI, a startup located in California. The technology was showcased at TechCrunch Disrupt, San Fransisco (2012) and MakerFaire – Bay Area (2013).

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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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