Generative Models · Multimodal Intelligence · Principled Learning

Shaoan Xie.

My research focuses on principled multimodal generative AI. I develop models that connect understanding of the world with generation, with an emphasis on controllability and generalization.

I received my Ph.D. from Carnegie Mellon University in August 2026, advised by Prof. Kun Zhang and Prof. Peter Spirtes. Previously, I interned at Adobe Research and Google.

Paper Cut portrait of Shaoan Xie
A little detour. Click to ride.
Illustrative process reward calculation: sample four completions from each denoising state. One of four earlier-state answers is correct versus three of four later-state answers. The estimated reward for this interval is 3/4 minus 1/4, or +0.5. These are illustrative numbers, not benchmark results.

ACL 2026 · Main

Improving reasoning in diffusion language models

We improve reasoning in diffusion LLMs by adding process rewards to GRPO-based reinforcement learning (RL) during post-training. These rewards compare answer success rates before and after a denoising interval, giving the model credit for progress toward a correct answer.

No human-labeled reasoning steps or separate reward model required.

Explore selected papers →

Academic leadership

  • ICLR 2026 & 2027 · Area Chair
  • NeurIPS 2026 · Area Chair

Publications

Google Scholar ↗

Generative Modeling & Editing

  1. ECCV
    Lingjing Kong*, Shaoan Xie*, Guangyi Chen, Yuewen Sun, Xiangchen Song, Eric P. Xing, Kun Zhang
    European Conference on Computer Vision, 2026.
    NeurIPS Mechanistic Interpretability Workshop, 2025.
  2. ICLR
    Yifan Shen*, Peiyuan Zhu*, Zijian Li, Shaoan Xie, Zeyu Tang, Namrata Deka, Zongfang Liu, Guangyi Chen, Kun Zhang
    International Conference on Learning Representations, 2026.
  3. ICML
    Shaoan Xie*, Lingjing Kong*, Yujia Zheng, Zeyu Tang, Eric P.Xing, Guangyi Chen, Kun Zhang
    International Conference on Machine Learning (ICML), 2025.
  4. arXiv
    Shaoan Xie, Yang Zhao, Zhisheng Xiao, Kelvin C.K. Chan, Yandong Li, Yanwu Xu, Kun Zhang, Tingbo Hou
    (arXiv), 2024.
  5. NeurIPS
    Yanwu Xu, Mingming Gong, Shaoan Xie, Wei Wei, Matthias Grundmann, Kayhan Batmanghelich, Tingbo Hou
    Advances in Neural Information Processing Systems (NeurIPS), 2023.
  6. CVPR
    Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz and Kun Zhang
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. Highlight
  7. CVPR
    Shaoan Xie, Yanwu Xu, Mingming Gong and Kun Zhang
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  8. ICLR
    Shaoan Xie, Lingjing Kong, Mingming Gong and Kun Zhang
    International Conference on Learning Representations (ICLR), 2023. Spotlight
  9. CVPR
    Yanwu Xu, Shaoan Xie, Wenhao Wu, and Kun Zhang, Mingming Gong, Kayhan Batmanghelich
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  10. NeurIPS
    Shaoan Xie, Qirong Ho and Kun Zhang
    Advances in Neural Information Processing Systems (NeurIPS), 2022.
  11. ICCV
    Shaoan Xie, Mingming Gong, Yanwu Xu and Kun Zhang
    IEEE/CVF International Conference on Computer Vision (ICCV), 2021.

Multimodal Learning & Reasoning

  1. ACL
    Shaoan Xie*, Lingjing Kong*, Xiangchen Song, Xinshuai Dong, Guangyi Chen, Eric P.Xing, Kun Zhang
    ACL main, 2026.
  2. ECCV
    Peiyuan Zhu, Shaoan Xie, Zijian Li, Yifan Shen, Namrata Deka, Harsh Shrivastava, Guangyi Chen, and Kun Zhang
    European Conference on Computer Vision, 2026.
  3. CVPR
    Shaoan Xie*, Lingjing Kong*, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P.Xing, Guangyi Chen, Kun Zhang
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. Highlight

Causal Learning & Generalization

  1. TMLR
    Shaoan Xie*, Biwei Huang*, Bin Gu, Tongliang Liu, Peter Spirtes, Kun Zhang
    Transactions on Machine Learning Research, 2026.
  2. CVPR
    Lingjing Kong, Shaoan Xie, Yang Jiao,Yetian Chen,Yanhui Guo, Simone Shao ,Yan Gao, Guangyi Chen, Kun Zhang
    CVPR, 2026.
  3. TAI
    Guanglin Zhou, Shaoan Xie, Guangyuan Hao, Shiming Chen, Biwei Huang, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao, Kun Zhang
    IEEE Transactions on Artificial Intelligence, 2025.
  4. ICML
    Yujia Zheng*, Shaoan Xie*, Kun Zhang
    International Conference on Machine Learning (ICML), 2025.
  5. IJCV
    Guanglin Zhou*, Zhongyi Han*, Shaoan Xie*, Shiming Chen, Biwei Huang, Liming Zhu, Xin Gao, Lina Yao, Salman Khan
    International Journal of Computer Vision , 2025.
  6. AISTATS
    Ignavier Ng, Shaoan Xie, Xingshuai Dong, Peter Spirtes, Kun Zhang
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
  7. ICLR
    Zijian Li, Shunxing Fan, Yujia Zheng, Ignavier Ng, Shaoan Xie, Guangyi Chen, Xinshuai Dong, Ruichu Cai, Kun Zhang
    International Conference on Learning Representations (ICLR), 2025.
  8. ICML
    Kun Zhang*, Shaoan Xie*, Ignavier Ng*, Yujia Zheng
    International Conference on Machine Learning (ICML), 2024.
  9. MICCAI
    Yanwu Xu, Shaoan Xie, Maxwell Reynolds, and Matthew Ragoza, Mingming Gong, Kayhan Batmanghelich
    International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022.
  10. ICML
    Lingjing Kong, Shaoan Xie, Weiran Yao, and Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang
    International Conference on Machine Learning (ICML), 2022. Spotlight
  11. ICML
    Shaoan Xie, Zibin Zheng, Liang Chen, and Chuan Chen
    International Conference on Machine Learning (ICML), 2018.

News

Academic service

Area Chair

ICLR ’26 & ’27, NeurIPS ’26

Session Chair

ICDM ’24

Conference Reviewer

ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, UAI, AISTATS, SIGGRAPH

Journal Reviewer

TPAMI, TIP, AI, CSUR, TNNLS, PR, TMM, JASA, TVCG, IJCV