ASME

Journal of Computing and Information Science in Engineering

Guang 
Lin, Ph.D.
Guang Lin

Areas of Interest

BIG DATA AND ANALYTICS
COMPUTATIONAL FLUID DYNAMICS
DATA-DRIVEN ENGINEERING APPLICATIONS
MACHINE LEARNING FOR ENGINEERING APPLICATIONS
NUMERICAL METHODS DEVELOPMENT FOR MODELING
UNCERTAINTY QUANTIFICATION
Purdue University
Dr. Guang Lin holds full professorship in the School of Mechanical Engineering and Department of Mathematics at Purdue University. He is the founding director of Data Science Consulting Service that performs cutting-edge research on data science and provides hands-on consulting support for data analysis and business analytics in all areas to overcome data science challenges arising in research, education, and business and organization management. He is also chair of Initiative in Data Engineering and Applications, co-director of Center for Intelligent Infrastructure, and University Faculty Scholar at Purdue University. His research interests include diverse topics in computational and data science both on algorithms and engineering applications. His main current thrust is machine learning for engineering applications, data-driven modeling for engineering applications, stochastic simulation, and multiscale modeling of interconnected, physical, biological, and engineering systems. He has published more than two hundred technical articles in journals, refereed conference proceedings, and edited books. He is a member of the American Society of Mechanical Engineers (ASME), and Society for Industrial and Applied Mathematics (SIAM). He serves as the associate editor of the ASME Journal of Computing and Information Science in Engineering and the associate editor of the SIAM Multiscale Modeling and Simulation. He has received numerous honors and awards for his scholarly contributions. Representative examples include CAREER Award from the National Science Foundation in 2016, Mid-Career Sigma Xi Award from Purdue University Chapter of Sigma Xi, the scientific research honor society in 2019, University Faculty Scholar from Purdue University in 2019, Dean’s Fellow from Purdue University in 2019, Mathematical Biosciences Institute Early Career Award from Mathematical Biosciences Institute in 2015, and Ronald L. Brodzinski Award for Early Career Exception Achievement from Pacific Northwest National Laboratory in 2012, ASCR Leadership Computing Challenge award from Department of Energy in 2010, Outstanding Performance Award at Pacific Northwest National Laboratory in 2010, and Ostrach Fellowship at Brown University in 2005. He has also received the best paper award in Engineered Science Materials and Manufacturing Journal in 2021.

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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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