Surya Kalidindi
Woodruff School of Mechanical Engineering and School of Computational Science and Engineering,
Georgia Institute of Technology, Atlanta GA
Surya R. Kalidindi earned a Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology and joined the Department of Materials Science and Engineering at Drexel University as an Assistant Professor, where he also served as the Department Head during 2000-2008. In 2013, Surya accepted a new position as a Professor of Mechanical Engineering in the George W. Woodruff School at Georgia Institute of Technology, with joint appointments in the School of Computational Science and Engineering and in the School of Materials Science and Engineering. Surya’s research efforts over the past two decades have made seminal contributions to the fields of crystal plasticity, microstructure design, spherical nanoindentation, and materials informatics. His work has already produced about 200 journal articles, four book chapters, and a new book on Microstructure Sensitive Design. His work is well cited by peer researchers as reflected by an h-index of 48 and current citation rate of about 1000 citations/year. He has recently been awarded the Alexander von Humboldt award in recognition of his lifetime achievements in research. He has been elected a Fellow of several professional societies: TMS, ASME, ASM International, and Alpha Sigma Mu.
Abstract
How CAN DATA SCIENCE ENABLE ACCELERATED DEVELOPMENT OF HIERARCHICAL MATERIALS
S. R. Kalidindi
Woodruff School of Mechanical Engineering and School of Computational Science and Engineering,
Georgia Institute of Technology, Atlanta, GA 30332
*email: surya.kalidindi@me.gatech.edu
The slow pace of new/improved materials development and deployment has been identified as the main bottleneck in the innovation cycles of most emerging technologies. The recent advances in data science can be leveraged suitably to address this impediment by effectively mediating between the seemingly disparate, inherently uncertain, multiscale and multimodal measurements and computations involved in the current materials development efforts. Proper utilization of modern data science in the materials development efforts can lead to a new generation of data-driven decision support tools for guiding effort investment (for both measurements and computations) at various stages of the materials development. It should also be recognized that the success of such ecosystems is predicated on the creation and utilization of integration platforms for promoting intimate, synchronous, collaborations between cross-disciplinary and distributed team members. This presentation provides a summary of recent advances made in our research group, and outlines specific directions of research that offer the most promising avenues.