Sung Yi is Professor and Chair of the Mechanical and Materials Engineering Department in the Maseeh College of Engineering and Computer Science at Portland State University. Previously, he was a faculty member and Head of the Engineering Mechanics Division at the School of Mechanical Engineering, Nanyang Technological University (NTU), Singapore. He was also a Singapore-MIT Alliance Fellow from 1998 to 2002. From 2006 to 2009, he served as Vice President at Manufacturing Engineering, R&D Institute, Samsung Electro-Mechanics, Republic of Korea. He graduated from Hanyang University in 1982, followed by obtaining his M.S. from the University of Washington and Ph.D. from the University of Illinois at Urbana-Champaign in 1987 and 1992, respectively. Dr. Yi's primary research interests are in semiconductor materials and manufacturing, computational mechanics, constitutive modeling and the characterization of electronic and engineering materials, 3D bioprinting, and machine learning for predicting the thermo-mechanical behaviors and failures of materials and systems.
Sung Yi is Professor and Chair of the Mechanical and Materials Engineering Department in the Maseeh College of Engineering and Computer Science at Portland State University. Previously, he was a faculty member and Head of the Engineering Mechanics Division at the School of Mechanical Engineering, Nanyang Technological University (NTU), Singapore. He was also a Singapore-MIT Alliance Fellow from 1998 to 2002. From 2006 to 2009, he served as Vice President at Manufacturing Engineering, R&D Institute, Samsung Electro-Mechanics, Republic of Korea. He graduated from Hanyang University in 1982, followed by obtaining his M.S. from the University of Washington and Ph.D. from the University of Illinois at Urbana-Champaign in 1987 and 1992, respectively. Dr. Yi's primary research interests are in semiconductor materials and manufacturing, computational mechanics, constitutive modeling and the characterization of electronic and engineering materials, 3D bioprinting, and machine learning for predicting the thermo-mechanical behaviors and failures of materials and systems.