CHEN Jie

CHEN Jie is an Associate Professor at Peking University's School of Electronic and Computer Engineering. His research focuses on Deep Learning, Computer Vision, and Pattern Recognition, with particular emphasis on representation learning and its applications in large-scale models. He has published extensively in top-tier journals and conferences, including Nature Machine Intelligence, TPAMI, IJCV, CVPR, ICCV, and NeurIPS. His work has been recognized in the "World’s Top 2% Scientists" list and has received significant citations. CHEN has contributed to numerous national research projects and has held editorial and organizational roles in leading conferences and journals.

Jie Chen is a researcher at Peking University with expertise in computer vision, large language models, and AI for Science, particularly in protein engineering. Their work spans object detection, image captioning, and robust image descriptors, with applications in medical imaging and remote sensing. Chen's research includes attention mechanisms, weakly-supervised learning, and domain adaptation for person re-identification. They have also explored skeleton-based action recognition and face synthesis across imaging modalities. Recent contributions include diffusion models for text-video retrieval and RNA language models for structural inference. Their publications highlight a focus on improving model efficiency, robustness, and cross-modal understanding.