Zichen Tian (Jason)

PhD candidate in Artificial Intelligence at Singapore Management University (SMU CVML Lab, advised by Prof. Qianru Sun). Previously research associate at S-Lab, Nanyang Technological University, and at Tsinghua University.

I study how foundation models acquire new knowledge from scarce data, and how to predict adaptation failures before training. In synthetic aperture radar (SAR), multispectral and medical imaging, data lie far from the pretraining distribution and labels are few and long-tailed, so the pretrained prior is indispensable yet mismatched. I treat adaptation as subspace allocation: deciding, from the pretrained weights and target data, where and along which directions an update should act, reusing transferable structure rather than disrupting it. This line of work includes debLoRA (NeurIPS 2024), MetaPEFT (CVPR 2025 Highlight) and mtLoRA (ICLR 2026). Methods: parameter-efficient fine-tuning (LoRA, PEFT), long-tailed and multi-task adaptation. Applications so far: SAR and multispectral remote-sensing imagery.

Honors

Selected Publications

All papers · llms.txt

Profiles: ORCID · Google Scholar · Semantic Scholar · OpenAlex · DBLP · Hugging Face · ResearchGate · GitHub