Shangyuan Tong

CS PhD at MIT CSAIL

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Welcome! I am a final-year Ph.D. candidate in Computer Science at MIT CSAIL advised by Tommi Jaakkola.

My current research focuses on making generative models, especially diffusion models, both more efficient and more scalable. During my doctoral studies, I have had the opportunity to intern at the MIT-IBM Watson AI Lab. I also interned at Google DeepMind, where I was fortunate to be mentored by Saining Xie.

Before MIT, I earned my B.S. from UC San Diego with a double major in Computer Science and Applied Mathematics, graduating Summa Cum Laude (GPA 3.96/4.0). There, I was first introduced to the world of research by Lawrence Saul.

I am on the industry job market and am excited to apply my research expertise to solving real-world challenges.

Contact: sytong [at] csail [dot] mit [dot] edu

Recent News [all]

Apr 05, 2025 Inference-Time Scaling for Diffusion Models is accepted to CVPR 2025 as a highlight paper! :tada:

Selected Publications [all]

  1. CVPR
    Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
    Nanye Ma*, Shangyuan Tong*, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, and Saining Xie
    In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2025
    Highlight
  2. ICLR
    Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
    Yilun Xu*, Shangyuan Tong*, and Tommi Jaakkola
    In International Conference on Learning Representations (ICLR), 2023
  3. ICLR
    Adversarial Support Alignment
    Shangyuan Tong*, Timur Garipov*, Yang Zhang, Shiyu Chang, and Tommi Jaakkola
    In International Conference on Learning Representations (ICLR), 2022
    Spotlight