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SUMMARY:Data Science Approaches for Mining Structure-Property-Processing L
 inkages from Large Datasets  
DTSTART:20150421T131500
DTEND:20150421T141500
DTSTAMP:20260408T071041Z
UID:34fc95c2133a92a4962cd028f429d77ff5858a29c8422b2744e53ba9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Surya R. Kalidindi\, George W. Woodruff School\, Georgia
  Institute of Technology\, USA\nBio : Surya R. Kalidindi earned a B.Tech. 
 in Civil Engineering from the Indian Institute of Technology\, Madras\, an
  M.S. in Civil Engineering from Case Western Reserve University\, and a Ph
 .D. in Mechanical Engineering from the Massachusetts Institute of Technolo
 gy. After his graduation from MIT in 1992\, Surya joined the Department of
  Materials Science and Engineering at Drexel University as an Assistant Pr
 ofessor\, where he served as the Department Head during 2000-2008. Under h
 is leadership\, the department experienced tremendous growth and was ranke
 d 10th nationally among Materials Science and Engineering programs by Acad
 emic Analysts in 2006. In 2013\, Surya accepted a new position as a Profes
 sor of Mechanical Engineering in the George W. Woodruff School at Georgia 
 Institute of Technology\, with joint appointments in the School of Computa
 tional Science and Engineering and in the School of Materials Science and 
 Engineering. Surya’s research efforts over the past two decades have mad
 e seminal contributions to the fields of crystal plasticity\, microstructu
 re design\, spherical nanoindentation\, and materials informatics. His wor
 k has already produced about 200 journal articles\, four book chapters\, a
 nd a new book on Microstructure Sensitive Design. His work is well cited b
 y peer researchers as reflected by an h-index of 50 and current citation r
 ate of about 1000 citations/year (Google Scholar). He has recently been aw
 arded the Alexander von Humboldt award in recognition of his lifetime achi
 evements in research.\nAbstract : Materials with enhanced performance char
 acteristics have served as critical enablers for the successful developmen
 t of advanced technologies throughout human history\, and have contributed
  immensely to the prosperity and well-being of various nations. Although t
 he core connections between the material’s internal structure (i.e. micr
 ostructure)\, its evolution through various manufacturing processes\, and 
 its macroscale properties (or performance characteristics) in service are 
 widely acknowledged to exist\, establishing this fundamental knowledge bas
 e has proven effort-intensive\, slow\, and very expensive for a number of 
 candidate material systems being explored for advanced technology applicat
 ions. It is anticipated that the multi-functional performance characterist
 ics of a material are likely to be controlled by a relatively small number
  of salient features in its microstructure. However\, cost-effective valid
 ated protocols do not yet exist for fast identification of these salient f
 eatures and establishment of the desired core knowledge needed for the acc
 elerated design\, manufacture and deployment of new materials in advanced 
 technologies. The main impediment arises from lack of a broadly accepted f
 ramework for a rigorous quantification of the material’s internal struct
 ure\, and objective (automated) identification of the salient features in 
 the microstructure that control the properties of interest.\nMaterials Inf
 ormatics focuses on the development of data science algorithms and computa
 tionally efficient protocols capable of mining the essential linkages in l
 arge multiscale materials datasets (both experimental and modeling)\, and 
 building robust knowledge systems that can be readily accessed\, searched\
 , and shared by the broader community. Given the nature of the challenges 
 faced in the design and manufacture of new advanced materials\, this new e
 merging interdisciplinary field is ideally positioned to produce a major t
 ransformation in the current practices. The novel data science tools produ
 ced by this emerging field promise to significantly accelerate the design 
 and development of new advanced materials through their increased efficacy
  in gleaning and blending the disparate knowledge and insights hidden in 
 “big data” gathered from multiple sources (including both experiments 
 and simulations). Our ongoing research has outlined a specific strategy fo
 r data science enabled development of new/improved materials\, and key com
 ponents of the proposed overall framework are illustrated with examples.
LOCATION:ME B3 31 http://plan.epfl.ch/?room=MEB331
STATUS:CONFIRMED
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