ASME

Journal of Computing and Information Science in Engineering

Krishnan 
Suresh, Ph.D.
SureshPassportPhoto

Areas of Interest

HIGH-PERFORMANCE COMPUTING
TOPOLOGY OPTIMIZATION
University of Wisconsin, Madison, USA
Krishnan Suresh is the Philip and Jean Myers Professor of Mechanical Engineering at the University of Wisconsin-Madison. He received a Master’s in Manufacturing Engineering from UCLA in 1992, and a Master’s and Ph.D. in Mechanical Engineering from Cornell in 1994 and 1998, respectively. He later served as an Engineering Manager at Kulicke and Soffa Industries, Philadelphia from 1998 to 2002. He has received numerous peer-reviewed grants, including the prestigious NSF Career award. His research interests include topology optimization, additive manufacturing, advanced finite element analysis and high-performance computing. He has co-authored over 75 peer-reviewed papers, two of which have received best-paper awards from ASME. He has also authored two text-books on applied optimization. He is the founder of SciArt, LLC (www.sciartsoft.com), a UW-Madison spinoff that creates and supports high-performance topology optimization software solutions.

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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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