Namuk Park

Email: namuk.park@gmail.com, GitHub: @xxxnell, Twitter: @xxxnell, CV: cv link, Google Scholar: scholar link

The focus of my research has been on understanding how deep neural networks work, and why they work that way, in order to build a more generalizable machine learning system. In particular, my research covers the following topics: (1) “empirical analysis of Vision Transformers” (through the lenses of loss landscape visualization, Hessian eigenvalue spectrum, Fourier analysis, etc.); (2) “probabilistic neural network” (e.g., Bayesian neural networks and ensemble methods); and (3) “generalization” and “trustworthy machine learning” (e.g., uncertainty estimation and robustness). Recently, I have been interested in (4) “self-supervised learning” (e.g., contrastive learning and masked image modeling) and (5) “AI for biology and science”.

Before joining Prescient Design, I previously worked at NAVER AI Lab as a visiting researcher. I received a Ph.D. in Computer Science from School of Integrated Technology, Yonsei University in South Korea, and graduated with a B.S. degree in Physics as Valedictorian of the College of Sciences from Yonsei University.

[4] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, Sangdoo Yun, “What Do Self-Supervised Vision Transformers Learn?” ICLR 2023.

[arxiv, code, poster, slides]

[3] Namuk Park and Songkuk Kim. “How Do Vision Transformers Work?” ICLR 2022. Spotlight.

[arxiv, code, poster, slides]

[2] Namuk Park and Songkuk Kim. “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness.” ICML 2022. Winner of Qualcomm Innovative Fellowship South Korea.

[arxiv, code, poster]

[1] Namuk Park, Taekyu Lee, and Songkuk Kim. “Vector Quantized Bayesian Neural Network Inference for Data Streams.” AAAI 2021.

[arxiv, code, poster, slides]