Keynote Speaker

Keynote Speaker

 

Keynote Speaker
  • Prof. Equo Kobayashi
    Department of Materials Science and Engineering Tokyo Institute of Technology, Japan
    Title: Development of biomedical Ti-Zr based alloys – Phase stability, microstructure and mechanical properties

Studying alloy designing of non-ferrous metals, such as titanium,
aluminum, copper and magnesium alloys.

Given some awards from scientific societies, such as Award for Young
Investigator of Japanese Society for Biomaterials, Light Metal R&D
Furtherance Medal of JILM, and JILM 70th Anniversary Service Medal. Also
given several times of the Best Paper Awards. Given TokyoTech Education
Award 2019 for education,

Working as Director or Councillor at several Japanese scientific
societies for materials.

Also working for international standardization of medical devices as a
Chairman of National Mirror Committee of ISO/TC84 since 2010.
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  • Prof. Teruyasu Mizoguchi
    Institute of Industrial Science, the University of Tokyo, Tokyo 153-8505, Japan
    Title: Machine learning approach for interface and surface

Teruyasu Mizoguchi is a professor in Institute of Industrial Science at the University of Tokyo, Tokyo, Japan.
He studied materials science at Kyoto University and obtained PhD in 2002. He was a JSPS research fellow at Kyoto University (2002-2003), the University of Tokyo (2003-2004), and Lawrence Berkeley National Laboratory (2004-2005). He was a research assistant (2005-2007) and an assistant professor (2007-2009) in School of Engineering, the University of Tokyo, and moved to the present institute in 2009 as an associated professor, and became professor in 2019. He has authored over 220 journal papers (H-index factor 41) and has delivered more than 50 plenary/keynote/invited lectures in international conferences. He got 12 prizes for his scientific and academic contributions, including young scientist award from Japan Institute of Metals, Ceramics Society Japan, and Japan Microscopy Societies.
His research interest is unveiling structure-property relationships through DFT simulation, atomic resolution analysis, and machine learning.
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