B.S. Mechanical and Aerospace Engineering, Seoul National University, 2009Ph.D. Mechanical and Aerospace Engineering, Seoul National University, 2013President’s Postdoc Fellow, Institute for Advanced Machinery Design, 2013-2015
"Geometry plays a critical role in science and engineering. Our mission is to quantify the roles played by shapes through the lenses of machine learning."
Stephen Baek is an Associate Professor of Data Science at the University of Virginia, where he also holds a courtesy appointment in the Department of Mechanical and Aerospace Engineering. Baek received his B.S. in Mechanical and Aerospace Engineering from Seoul National University (SNU), Seoul, Korea in 2009 and a Ph.D. in 2013 from the same institution for his award-winning study on the statistical space of shapes modeled on a Riemannian manifold. He was a postdoctoral researcher at the Institute for Advanced Machinery Design in SNU from 2013 to 2015 and a visiting research associate at the Ronald E. McNair Center for Aerospace Innovation and Research at the University of South Carolina in 2014. During his training, Baek received the National Science and Engineering Scholarship and the Global Ph.D. Fellowship from the Korean Ministry of Education. He was also awarded the Presidential Postdoc Fellowship from the President of the Republic of Korea. Prior to joining the University of Virginia, Baek spent 6 years as an Assistant Professor at the University of Iowa.
Awards
Shannon Center Mid-Career Faculty Fellow2023
Associate Editor, Journal of Computational Design and Engineering 2021
University of Iowa Supervisor of the Year Award 2021
Defense Innovation Award2021
University of Iowa Innovator Award2019
Old Gold Summer Fellowship 2018
Best Paper Award, International Conference on Maintenance and Rehabilitation of Constructed Infrastructure Facilities2017
Delcam Korea Best Graduate Thesis Award2014
Presidential Postdoc Fellowship2014
Global Ph.D. Fellowship, Korean Ministry of Education2011
Bronze Medal, Korea Software Awards2009
National Science and Engineering Scholarship, Korean Ministry of Education2005
Research Interests
Geometric Data Analysis
Statistical Shape Modeling & Analysis
Deep Learning on Non-Euclidean Domains
Data-driven Design and Engineering
Digital Human Modeling
Selected Publications
PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials ABSScience Advances, 9(17): eadd6868. (2023)
Body shape matters: Evidence from machine learning on body shape-income relationship ABSPLOS One, 16(7): e0254785. (2021)
Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials ABSScientific Reports, 10: 13307. (2020)
ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation ABSComputer Graphics Forum, 39(6): 204-216. (2020)
Deep segmentation networks predict survival of non-small cell lung cancer ABSScientific Reports, 9: 17286. (2019)
Parametric human body shape modeling framework for human-centered product design ABSComputer-Aided Design, 44(1): 56-67. (2012)