Stephen Baek

Stephen Baek is an Associate Professor of Data Science at the University of Virginia, with a courtesy appointment in the Department of Mechanical and Aerospace Engineering. He holds a B.S. and Ph.D. in Mechanical and Aerospace Engineering from Seoul National University (2009–2013), followed by postdoctoral research at SNU and the University of South Carolina. Prior to UVA, he served as an Assistant Professor at the University of Iowa for six years. His research focuses on geometric data analysis, statistical shape modeling, deep learning on non-Euclidean domains, and digital human modeling, with applications in materials science, medical imaging, and engineering design.

Education:

• B.S., Mechanical and Aerospace Engineering, Seoul National University, 2009
• Ph.D., Mechanical and Aerospace Engineering, Seoul National University, 2013
• President’s Postdoc Fellow, Institute for Advanced Machinery Design, 2013–2015

Research emphasizes the intersection of geometry and machine learning, particularly in quantifying shape roles through statistical and deep learning methods. Key contributions include physics-aware neural networks (PARC) for energetic materials modeling and ZerNet for surface analysis on arbitrary geometries.

Recent Research Trends: Publications highlight advancements in federated learning for medical data, generative models for microstructure design, and AI-driven analysis of human physiology. Work spans from materials-by-design frameworks to clinical applications like velopharyngeal segmentation and injury prediction systems.

Awards & Recognition:

• Shannon Center Mid-Career Faculty Fellow (2023)
• University of Iowa Supervisor of the Year (2021)
• Defense Innovation Award (2021)
• Global Ph.D. Fellowship (2011)
• Korean Ministry of Education Scholarships (2005–2011)

Advising and Grants: Supervised multiple students in data science and engineering, with active grants focused on AI in materials science and medical diagnostics. Leads the Visual Intelligence Laboratory, collaborating on projects like HEDS (heterogeneous energetic materials simulator) and AI-driven clinical tools.

Labs & Teams: Directs the Visual Intelligence Laboratory, focusing on computational design, medical imaging AI, and physics-aware machine learning systems. Collaborates with industry partners and academic networks globally.