Department News

Agrawal Gives Keynote Lecture for Materials Genome Symposium at CMC 2017

His talk was titled, "Materials Informatics and Big Data: Realization of 'Fourth Paradigm' of Science in Materials Science."

Jul 25, 2017

Prof. Ankit Agrawal

Prof. Ankit Agrawal was recently invited to give a keynote lecture about his research on materials informatics and data mining at the Materials Genome Symposium of Chinese Materials Conference (CMC) 2017, held July 6-12 in Yinchuan, Nigxia, China. His talk was titled, "Materials Informatics and Big Data: Realization of 'Fourth Paradigm' of Science in Materials Science." Prof. Agrawal's research in the field of materials informatics is supported by NIST CHiMaD, AFOSR MURI, DARPA SIMPLEX, NSF Big Data Spoke, and NU-DSI grants.

Chinese Materials Conference is the most important meeting series of Chinese Materials Research Society (C-MRS). Among the 35 symposia and 2 international materials forums of Chinese Materials Conference 2017, “Materials Genomes” was the first of its kind symposium in this series, and was focused on accelerating the development of materials using high-throughput computation, high-throughput experiment, and materials informatics & materials databases.

Talk Abstract: Our ability to collect “big data” has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Materials Genome Initiative (MGI), further boosting the emerging field of materials informatics. In this article, we look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery). Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.

Prof. Agrawal is a Research Associate Professor of EECS at Northwestern University. His research on high performance big data mining aims at a coherent integration of high performance computing and data mining to develop customized solutions for big data problems with an emphasis on interdisciplinary impact. His research has contributed to large-scale data-guided discovery in various science/engineering disciplines, such as materials science, healthcare, bioinformatics, and social media, with several interdisciplinary collaborations with domain scientists in respective fields. He has co-authored 100+ peer-reviewed papers, including those at top-tier computer science venues such as KDD, ICDM, CIKM, SDM, ICDE, Supercomputing, HiPC, and interdisciplinary ones like Nature Scientific Reports, APL Materials, npj Computational Materials, JAMIA, PRB, and TCBB. He has also developed and released several open-source software, been on program committees of major research conferences, and is serving as a PI/Co-PI on funded research grants from NSF, DOE, AFOSR, NIST, DARPA, and NU-DSI.

McCormick News Article