ASME

Journal of Computing and Information Science in Engineering

Stephen 
Baek, Ph.D.
Stephen Baek_2022

Areas of Interest

(GEOMETRIC) DEEP LEARNING
BIG DATA AND ANALYTICS
COMPUTATIONAL DESIGN
COMPUTATIONAL GEOMETRY
COMPUTER VISION
GEOMETRIC DATA ANALYSIS
University of Virginia, USA
Stephen Baek is an applied geometer and a data scientist who conducts research in ‘geometric data analysis.’ At the intersection of computational geometry, vision, and machine learning, Baek studies a wide range of multidisciplinary problems, in which shapes play an important role in understanding scientific phenomena. His previous and ongoing research projects include AI-assisted design of mechanical systems, linkages between microstructures and mechanical behaviors of material systems, relationships between geometric features and manufacturability, roles of tumoral and peritumoral geometry on cancer treatment outcome, socioeconomic bias associated with physical appearance, etc. He has published more than a hundred papers and abstracts in journals and conference proceedings. He has been leading various research projects sponsored by federal/local governments and industry, including the National Science Foundation (NSF), National Institutes of Health (NIH), U.S. Department of Defense, NASA, U.S. Department of Transportation, Hyundai Motor Company, and others. He received various honors and awards including the Korean National Science and Engineering Scholarship, Presidential Postdoc Fellowship, and several best paper awards from conferences and journals. Currently, he is an associate professor with tenure at the University of Virginia with appointments in the School of Data Science (primary) and the School of Engineering & Applied Science.

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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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