Dr. Yan Wang is a research assistant professor at the Institute for AI Industry Research (AIR), Tsinghua University. His research interests include data compression powered by AI, perception in intelligent transportation and neural network compression. Before joining AIR, he was a senior researcher and team leader at SenseTime, where he and his team focus on research and product development in the fields of object detection, model compression and data compression powered by AI. He has extensive interdisciplinary knowledge and research experience in the fields of artificial intelligence, parameter estimation, computational fluid dynamics and public safety.
Profile：
https://yanwang202199.github.io
Email address:
wangyan@air.tsinghua.edu.cn
Education:
2014-2019 Tsinghua University, Safety Science and Engineering, PhD
2017-2018 Cornell University, Civil and Environmental Engineering Visiting, PhD
2010-2014 Tsinghua University, Engineering Physics, Bachelor
Career:
2022-now Institute for AI Industry Research (AIR), Tsinghua University, Research Assistant Professor
2019-2021 SenseTime. Researcher, Senior Researcher
Research Fields:
Data compression powered by AI, perception in intelligent transportation, neural network compression
Selected Publications:
1. Zeng, Fanhu, Hao Tang, Yihua Shao, Siyu Chen, Ling Shao, Yan Wang. MambaIC: State Space Models for High-Performance Learned Image Compression, CVPR, 2025.
2. Xu, Tongda, Jiahao Li, Bin Li, Yan Wang, Ya-Qin Zhang, Yan Lu. PICD: Versatile Perceptual Image Compression with Diffusion Rendering, CVPR 2025.
3. Xu, Tongda, Xiyan Cai, Xinjie Zhang, Xingtong Ge, Dailan He, Ming Sun, Jingjing Liu, Ya-Qin Zhang, Jian Li, and Yan Wang. Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior, ICLR, 2025.
4. Li, Xiang, Pengfei Li, Yupeng Zheng, Wei Sun, Yan Wang, and Yilun Chen. Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving, ICLR, 2025.
5. Zhang, Xinjie, Shenyuan Gao, Zhening Liu, Jiawei Shao, Xingtong Ge, Dailan He, Tongda Xu, Yan Wang, and Jun Zhang. Content-aware Masked Image Modeling Transformer for Stereo Image Compression, AAAI, 2025.
6. Chen, Yuan, Zi-han Ding, Ziqin Wang, Yan Wang, Lijun Zhang, and Si Liu. Asynchronous Large Language Model Enhanced Planner for Autonomous Driving, ECCV, 2024.
7. Du, Penghui, Yu Wang, Yifan Sun, Luting Wang, Yue Liao, Gang Zhang, Errui Ding, Yan Wang, Jingdong Wang, and Si Liu. LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction, ECCV, 2024.
8. Zhang, Xinjie, Xingtong Ge, Tongda Xu, Dailan He, Yan Wang, Hongwei Qin, Guo Lu, Jing Geng, and Jun Zhang. GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting, ECCV, 2024.
9. Zhang, Xinjie, Ren Yang, Dailan He, Xingtong Ge, Tongda Xu, Yan Wang, Hongwei Qin, and Jun Zhang. Boosting Neural Representations for Videos with a Conditional Decoder, CVPR, 2024. Highlight.
10. Ge, Xingtong, Jixiang Luo, Xinjie Zhang, Tongda Xu, Guo Lu, Dailan He, Jing Geng, Yan Wang, Jun Zhang, and Hongwei Qin. Task-Aware Encoder Control for Deep Video Compression, CVPR, 2024.
11. Tongda Xu, Dailan He, Ziran Zhu, Yanghao Li, Lina Guo, Yuanyuan Wang, Zhe Wang, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang, Idempotence and Perceptual Image Compression, ICLR, 2024. spotlight.
12. Yanghao Li, Tongda Xu, Yan Wang, Jingjing Liu, Ya-Qin Zhang, Idempotent Learned Image Compression with Right-Inverse, NeurIPS, 2023.
13. Tongda Xu, Han Gao, Chenjian Gao, Yuanyuan Wang, Dailan He, Jinyong Pi, Jixiang Luo, Ziyu Zhu, Mao Ye, Hongwei Qin, Yan Wang, Jingjing, Ya-Qin Zhang, Bit allocation using optimization, ICML, 2023.
14. Tongda Xu, Yan Wang, Dailan He, Chenjian Gao, Han Gao, Kunzan Liu, Hongwei Qin, Multi-Sample Training for Neural Image Compression, NeurIPS, 2022.
15. Chenjian Gao, Tongda Xu, Dailan He, Yan Wang, Hongwei Qin, Flexible Neural Image Compression via Code Editing, NeurIPS, 2022.
16. Lina Guo, Xinjie Shi, Dailan He, Yuanyuan Wang, Rui Ma, Hongwei Qin, Yan Wang, Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain, CVPR, 2022.
17. Dailan He, Ziming Yang, Weikun Peng, Rui Ma, Hongwei Qin, Yan Wang, ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding, CVPR, 2022. oral.
18. Dailan He, Yaoyan Zheng, Baocheng Sun, Yan Wang, Hongwei Qin, Checkerboard context model for efficient learned image compression, CVPR, 2021.
19. Rundong Li, Yan Wang, Feng Liang, Hongwei Qin, Junjie Yan, Rui Fan, Fully quantized network for object detection, CVPR, 2019.
Major Honors and Awards：
2022 Champion of CVPR Challenge on Learned Image Compression (CLIC)
2018 ISCRAM Best Paper
2018 SenseTime Future Star
2014 Tsinghua Doctoral Fellowship for Future Scholar
