ASME

Journal of Computing and Information Science in Engineering

Samy  
Missoum
SammyPS

Areas of Interest

TOPOLOGY OPTIMIZATION
Samy Missoum is a Professor in the Aerospace and Mechanical Engineering Department at The University of Arizona where he leads the Computational Optimal Design of Engineering Systems (CODES) laboratory. He is an expert in simulation-based design optimization and probabilistic design, with over 25 years of experience. His research focuses on the development of approaches for the reliability, risk assessment, and optimization of complex and highly nonlinear engineering problems, including structural impact, nonlinear vibrations, and aerodynamics. His research has been funded by AFOSR, NSF, NIH, DOE, DOD, and industry. He is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) and was an Associate Editor for the ASME Journal of Mechanical Design. He received his Doctorate from the National Institute of Applied Sciences in Toulouse, France and his M.S. in Computer-Aided Engineering from Strathclyde University, Glasgow, U.K.

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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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