I am a Postdoc Researcher at Princeton University, fortunately working with Prof. Mengdi Wang. My research focuses on advanced generative models, including both methodological development and real-world applications. I received my Ph.D. degree from Peking University, supervised by Prof. Bin Cui and Prof. Luxia Zhang. I was also fortunate to collaborate with Yang Song, Guohao Li, Shuicheng Yan, Ming-Hsuan Yang, Bernard Ghanem, Stefano Ermon, and Jure Leskovec. Please feel free to reach out for potential collaborations or discussions.
Email | WeChat | Github | Google Scholar | Twitter

We have opening positions for PhDs, Masters and Research Interns (Princeton University, available in-person and remote). Also, I am in charge of a reasearch team and have led a series of works on Diffusion Models and LLMs, including RPG-DiffusionMaster, MMaDA, Diffusion-Survey, Buffer of Thoughts, ReasonFlux, VideoTetris, Consistency Flow Matching, IterComp. Interested persons please contact me directly!

Research Summary

My goal is to build powerful generative models capable of understanding, generating and reasoning with high-dimensional data across diverse modalities. I currently focus on developing advanced generative models, including their training methodologies, architecture design, alignment, inference efficiency and applications.

Generative Model Foundations

• Diffusion Theory and Framework: RPG, MMaDA, ContextDiff, Consistency Flow Matching, Diffusion-Sharpening, Rectified Diffusion, ConPreDiff, SADM
• LLM/MLLM Framework: Buffer of Thought, MMaDA, SuperCorrect, HermesFlow, ReasonFlux, ReasonFlux-Coder
• Agent Framework: ScoreFlow, Multi-Actor Collaborative Data Selection
• Published Book: “Diffusion Model: Theory, Application, and Code Practice of Generative AI Models”

Generative Applications

• Multimodal Generation (Image/3D/4D): IterComp, VideoTetris, EditWorld, Trans4D, WideRange4D
• Scientific Applications: IPDiff, IRDiff, BindDM

What's New

• 1 paper about LLMs is accepted by ACL 2025.
• I was invited as an Area Chair at NeurIPS 2025.
• 6 papers about LLMs and Diffusion Models are accepted by ICLR 2025.
• 5 papers about Diffusion Models and LLMs are accepted by NeurIPS 2024, including one Spotlight paper.
• 2 papers about Diffusion Models and AI for Science are accepted by ICML 2024.
• 1 paper about general/molecular graph diffusion is accepted by TKDE 2024.
• 1 paper about improved training algorithm of Diffusion Transformers (DiT), DDPMs and Score SDEs is accepted by CVPR 2024.
• 3 papers about Diffusion Models, GNN, AI for Science are accepted by ICLR 2024.
• 1 paper about molecular diffusion models is accepted by AAAI 2024.
• 1 paper about diffusion model survey collaborating with OpenAI is accepted by ACM Computing Surveys.
• 1 paper about diffusion models is accepted by NeurIPS 2023.
• I publish a book about Diffusion Models.
• 1 paper is accepted by TNNLS 2023.
• 1 paper is accepted by TKDE 2023.
• 2 papers are accepted as ICML 2022 Spotlight.
• 1 paper is accepted by CVPR 2020.

Selected Papers [Full List]

• Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
  Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E Gonzalez, Bin Cui
  NeurIPS 2024 spotlight paper | code | tweet

• MMaDA: Multimodal Large Diffusion Language Models
  Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, Mengdi Wang
  paper | code | tweet

• ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
  Ling Yang, Zhaochen Yu, Bin Cui, Mengdi Wang
  paper | code | tweet

• Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs.
  Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui
  ICML 2024 paper | code | tweet

• SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction.
  Ling Yang, Zhaochen Yu, Tianjun Zhang, Minkai Xu, Joseph E. Gonzalez, Bin CUI, Shuicheng YAN
  ICLR 2025 paper | code

• Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing
  Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin CUI
  ICLR 2025 paper | code

• Structure-Guided Adversarial Training of Diffusion Models
  Ling Yang, Haotian Qian, Zhilong Zhang, Jingwei Liu, Bin CUI
  CVPR 2024 paper

• Improving Diffusion-Based Image Synthesis with Context Prediction
  Ling Yang, Jingwei Liu, Shenda Hong, Zhilong Zhang, Zhilin Huang, Zheming Cai, Wentao Zhang, Bin CUI
  NeurIPS 2024 paper

• Diffusion Models: A Comprehensive Survey of Methods and Applications
  Ling Yang, Zhilong Zhang, Yang Song (OpenAI), Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin CUI, Ming-Hsuan Yang
  ACM Computing Surveys 2023 paper

• IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation
  Xinchen Zhang*, Ling Yang*, Guohao Li, Yaqi Cai, Jiake Xie, Yong Tang, Yujiu Yang, Mengdi Wang, Bin Cui
  ICLR 2025 paper | code | tweet

• Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
  Fu-Yun Wang, Ling Yang, Zhaoyang Huang, Mengdi Wang, Hongsheng Li
  ICLR 2025 paper | code

• Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
  Ling Yang, Zixiang Zhang, Zhilong Zhang, Xingchao Liu, Minkai Xu, Wentao Zhang, Chenlin Meng, Stefano Ermon, Bin Cui
  paper | code | tweet

• VideoTetris: Towards Compositional Text-to-Video Generation
  Ye Tian*, Ling Yang*, Haotian Yang, Yuan Gao, Yufan Deng, Jingmin Chen, Xintao Wang, Zhaochen Yu, Xin Tao, Pengfei Wan, Di Zhang, Bin Cui
  NeurIPS 2024 paper | code | tweet

• VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
  Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin Cui, Muhan Zhang, Jure Leskovec
  ICLR 2024 paper | code

• Dpgn: Distribution propagation graph network for few-shot learning
  Ling Yang, Liangliang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou, Yu Liu
  CVPR 2020 paper | code

Advising Experience

Zhaochen Yu (Master student at National University of Singapore)

Xinchen Zhang (Master student at Tsinghua University)

Ye Tian (incoming Ph.D. student at PKU).

Zhilin Huang (Ph.D. student at Tsinghua University).

Zhilong Zhang (incoming Ph.D. student at Tsinghua University)

Yinjie Wang (Ph.D. student at The University of Chicago).

Academic Services

• Area Chair:
  • NeurIPS 2025
• Program Committee or Reviewer:
  • ICML 2025, ICLR 2025, CVPR 2025, ICCV 2025, AAAI 2025
  • SIGGRAPH 2024, ICML 2024, ICLR 2024, NeurIPS 2024, CVPR 2024, AAAI 2024
  • ICML 2023, ICLR 2023, NeurIPS 2023, CVPR 2023, AAAI 2023
  • ICML 2022, ICLR 2022, NeurIPS 2022
• Journal Reviewer
  • ACM Computing Surveys (CSUR)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)
  • IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  • Pattern Recognition (PR)

Awards

• Completed my Ph.D. degree as an Outstanding Graduate in Peking University.
• Selected as the KAUST Rising Stars in AI Symposium (24 people in the world), 2025.
• Selected for AI Elite Forum of WAIC (20 people in the world), 2024.
• Selected for the distinguished student forum of VALSE (8 People in China), 2024.
• Selected for Annual Outstanding Author of Electronics Industry Press, 2023.
• Selected for two consecutive years in the TechBeat Influencers List (2023 list and 2024 list, 20 people in China).
• Baidu Scholarship Nominee (20 people in the world), 2023.
• National Scholarship for Ph.D student (Top 1% in PKU), 2022.
• Exceptional Award for Academic Innovation for Ph.D student (Top 1% in PKU), 2022.
• First-class Academic Scholarship, 2018, 2019, 2020.