ASME

Journal of Computing and Information Science in Engineering

Yong 
Chen, Ph.D.
YONG_CHEN

Areas of Interest

ADDITIVE MANUFACTURING
COMPUTER-AIDED MANUFACTURING
MANUFACTURING AUTOMATION
University of Southern California, USA
Dr. Yong Chen is a professor of Industrial and Systems Engineering and Aerospace and Mechanical Engineering at the University of Southern California (USC). Dr. Chen’s research focuses on additive manufacturing (3D printing) in micro- and meso- scales. He received multiple Best/Outstanding Paper Awards in major design and manufacturing journals and conferences. Other major awards he received include the National Science Foundation Faculty Early Career Development (CAREER) Award, the Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers (SME), and the invitations to the National Academy of Engineering (NAE) Frontiers of Engineering Symposiums. Dr. Chen is a Fellow of the American Society of Mechanical Engineers (ASME). He has served as conference/program chairs as well as keynote speakers in several international conferences.

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Announcements

July 17 Spotlight: “Information Embedding in Additively Manufactured Parts Through Printing Speed Control” 

A recording is now available for the July 17, 2024 JCISE Spotlight talk by Professor Jitesh Panchal on paper co-authored with Karim A. ElSayed entitled “Information Embedding in Additively Manufactured Parts Through Printing Speed Control” J. Comput. Inf. Sci. Eng. J. Comput. Inf. Sci. Eng. Jul 2024, 24(7): 071005 (10 pages) Paper No: JCISE-23-1496 https://doi.org/10.1115/1.4065089.

Announcements

June 18, 2024 Spotlight: “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset”

A recording is now available on Youtube for the June 18, 2024 Spotlight talk by Professor Christian Soize (Université Gustave Eiffel) on his paper “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset,”

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