I am a final-year Ph.D. candidate at KAIST, advised by Jinwoo Shin, and a visiting scholar at NYU working with Saining Xie. During my Ph.D., I interned at Google Research/DeepMind and NVIDIA Research to design efficient video generation models. I also collaborated with Kihyuk Sohn through the university relations program at Google Research.
I am broadly interested in a range of problems in computer vision. In particular, my research focuses on the intersection of visual representation learning and generative modeling towards unified, human-like vision models capable of both creating and understanding complex visual worlds. I am also interested in AI4Science and have contributed to several related projects.
Jun 2025: I am attending CVPR in person. See you in Nashville! Apr 2025: I received Google Conference Scholarships (APAC). Apr 2025: I am attending ICLR in person. See you in Singapore! Mar 2025: I gave a talk at Sheffield NLP. Feb 2025: BootComp and CoordTok are accepted to CVPR 2025. Jan 2025: REPA is accepted to ICLR 2025 as an oral presentation! Oct 2024: I gave a talk at BioImaging, Signal Processing & Learning Lab at KAIST. Jun 2024: I am attending CVPR in person. Looking forward to seeing you in Seattle! |
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Google Research
NVIDIA Research
Google Research |
Recipient, ICLR 2025 Google Conference Scholarships (APAC)
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MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation
Training Diffusion Transformers Is Easier Than You Think
Efficient Latent Video Diffusion Models via Triplane Encoding
Video Probabilistic Diffusion Models in Projected Latent Space
Efficient Generative Models for Videos
RoMA: Robust Model Adaptation for Offline Model-based Optimization
Scaling Video Generation via Implicit Neural Representations
Abstract Reasoning via Logic-guided Generation |
Conference reviewer: ICML'22-25, NeurIPS'22-25, ICLR'23-25, CVPR'23-25, ICCV'23-25, ECCV'24, WACV'25, AAAI'25, AISTATS'25
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Teaching Assistant, Deep Learning, Fall 2020
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