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Faculty
Stephen Baek
Professor of Data Science
School of Data Science
Additional Roles
Professor by Courtesy
Department of Mechanical and Aerospace Engineering
Email:
baek@virginia.edu
Additional Contact Information
Website: www.stephenbaek.com
Office: 108 Cresap, Rm. 104
LinkedIn: https://www.linkedin.com/in/stephenbaek/
Baek is an applied geometer, scientist, and entrepreneur. He studies the space of shapes using machine learning. Baek’s educational background is in mechanical and aerospace engineering.
Prior to joining UVA in 2021, he was an Assistant Professor at the University of Iowa, where he taught courses on deep learning. There, he also founded and directed the Visual Intelligence Laboratory, which conducts fundamental research in computational geometry, vision, and machine learning. Baek’s research interests include geometric data analysis, geometric deep learning, scientific machine learning, and data-driven design.
Baek’s published research is extensive, including “Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials” and “Deep segmentation networks predict survival of non-small cell lung cancer.”
Baek holds a PH.D. in Mechanical and Aerospace Engineering from Seoul National University and a B.S. in Mechanical and Aerospace Engineering from Seoul National University.
Education
- PH.D., Mechanical and Aerospace Engineering, Seoul National University
- B.S., Mechanical and Aerospace Engineering, Seoul National University
Data Science Domains
Analytics
Areas of Practice
Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Computational Sciences; Engineering, Robotics, and Physical Sciences ; Sports Science and Analytics
Methodologies
Geometric Data Analysis; Computational Geometry; Computer Vision
Selected Publications
- Cheng, X., Nguyen, P. C., Seshadri, P. K., Verma, M., Gray, Z. J., Beerman, J. T., Udaykumar, H., & Baek, S. S. (2024). Physics-aware recurrent convolutional neural networks for modeling multiphase compressible flows. International Journal of Multiphase Flow, 177:104877.
- Nguyen, P. C., Cheng, X., Azarfar, S., Seshadri, P., Nguyen, Y. T., Kim, M., Choi, S., Udaykumar, H., & Baek, S. (2024). PARCv2: Physics-aware recurrent convolutional neural networks for spatiotemporal dynamics modeling. In The 41st International Conference on Machine Learning (ICML 2024). Vienna, Austria.
- Choi, J. B., Nguyen, P. C., Sen, O., Udaykumar, H., & Baek, S. (2023). Artificial intelligence approaches for energetic materials by design: state of the art, challenges, and future directions. Propellants, Explosives, Pyrotechnics, 48(4): e202200276.
- Nguyen, P. C., Nguyen, Y.-T., Seshadri, P. K., Choi, J. B., Udaykumar, H., & Baek, S. (2023). A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials. Propellants, Explosives, Pyrotechnics, 48(4): e202200268.
- Nguyen, P. C., Vlassis, N. N., Bahmani, B., Sun, W., Udaykumar, H., & Baek, S. (2022). Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning. Scientific Reports, 12: Article No. 9034.
- Song, S. & Baek, S. (2021). Body shape matters: Evidence from machine learning on body shape-income relationship. PLOS ONE, 16(7): 1–17.
- Sun, Z., Rooke, E., Charton, J., He, Y., Lu, J., & Baek, S. (2020). ZerNet: Convolutional neural networks on arbitrary surfaces via Zernike local tangent space estimation. Computer Graphics Forum, 39(6): 204–216.
- Chun, S., Roy, S., Nguyen, Y. T., Choi, J. B., Udaykumar, H. S., & Baek, S. (2020). Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials. Scientific Reports, 10: Article No. 13307.