Yuxiao Dong @ericdongyx
Associate Professor
Computer Science, Tsinghua
Publications | Students | Talks | Services | Contact | Top
🍄 🍄 🍄 🍄 🍄 🍄 🍄 🍄

Yuxiao is an Associate Professor of Computer Science at Tsinghua University. His research focuses on large language models, data mining, graph representation learning, and social and information networks. Together with collaborators, his recent work on LLM/VLM pre-training, reasoning, agents and RL scaling has been transferred into several open-sourced LLMs and products. Earlier in his career, his work on graph representation learning and pre-training was deployed for large-scale applications in Facebook, Microsoft, Tencent, and nominated for best papers in WWW'22, WWW'19, ECML-PKDD'23, and WSDM’15. He received his Ph.D. in Computer Science from University of Notre Dame, and before joining Tsinghua, he was a researcher at Meta AI and Microsoft Research Redmond. He has been very fortunate to receive the 2017 SIGKDD Dissertation Award Honorable Mention and the 2022 SIGKDD Rising Star Award. He is also very proud to be selected as one of the ten most favorite teachers in 2024 --- a recognition voted on by students once every two years.

Here are some of recent works on LLMs:

  1. llm/vlm reasoning & agent: AgentRL, DeepDive, MobileRL, WebRL, T1 for RL Scaling, TreeRL, SWE-Dev, AndroidLab, AgentBench, AgentTuning
  2. llm/vlm alignment: VisionReward, ImageReward, Scaling RLHF
  3. llm/vlm pre-training: Emergent Abilities vs. Loss, CogVLM, Speech-Text Pre-training, CogView3, CodeGeeX

Here are recent works on Graph Pre-Training & Graph Representation Learning, some of which were used in Facebook, Instagram, Microsoft, Tencent Games, and Alibaba:

  1. graph pre-training: GraphAlign, GraphMAE, GraphMAE2, GPT-GNN, GCC
  2. network embedding: NetMF, NetSMF, ProNE, SketchNE
  3. heterogeneous graphs: metapath2vec, Heterogeneous Graph Transformer (HGT)

More information about his experience and research can be found on LinkedIn and Google Scholar.

Students & Interns

I'm always looking for self-motivated students to work on large language models, vision language models, LLM agent, and graphs, networks. It would be great if your institutional email address is used for initial contact, if having one. Reference letters for applying for graduate schools (of at most ten universities) can be happily provided upon 1) solid outputs AND 2) at least 16 weeks of collaboration (the duration of both one semester at Tsinghua and a MSR/Facebook summer research internship).

I'm very lucky to have the opportunities to work with these brilliant students and interns (ordered by year, with their representative work done together).

  1. 2024-, PhD student: Mingdao Liu
  2. 2023-, PhD students: Yifan Xu, Xiao Xia
  3. 2022-, PhD student: Jiazheng Xu: ImageReward (NeurIPS'23, PyPI 下载量 )
  4. 2021/2023-, PhD student: Zhenyu Hou: GraphMAE (most cited in KDD'22), T1: LLM RL & Test-Time Scaling (ICML'25), won the 2025 Ant PhD Fellowship (ten globally)
  5. 2022-, Undergraduate students:
    1. Yujiang Li (PhD@Tsinghua, 2025-) | Rui Lu (PhD@Tsinghua, 2025-) | Zhilei Bei (PhD@MIT, 2025-) | Heyang Jiang (PhD@UCLA, 2025-) | Yuxuan Tong (ByteDance Seed, 2025-)
    2. Mingdao Liu (PhD@Tsinghua, 2024-) | Guanghan Wang (PhD@Cornell, 2024-) | Yifan An (ZhipuAI, 2024-)
    3. Xiao Xia (PhD@Tsinghua, 2023-): CodeGeeX (2nd co-first author, most cited in KDD'23) | Shiyu Zhao (Master@Stanford, 2023-) | Weikai Li (PhD@UCLA, 2023-) | Ziang Li (PhD@GaTech, 2023-)
  6. 2020, summer interns@MSR: Namyong Park, 3rd year Ph.D. student at CMU | Scott Freitas (co-mentor), 3rd year Ph.D. student at Georgia Tech | Yu Zhang (co-mentor), 3rd year Ph.D. student at UIUC: 2025 SIGKDD Dissertation Award Runner-Up
  7. 2019, summer intern@MSR: Ziniu Hu, 1st year Ph.D. student at UCLA: Heterogeneous Graph Transformer (HGT) (most cited in WWW'20), GPT-GNN (KDD'20), won the 2024 SIGKDD Dissertation Award Runner-Up
  8. 2017, summer intern@MSR: Jiezhong Qiu, 1st year Ph.D. student at Tsinghua: NetMF (2nd most cited in WSDM'18), NetSMF (best paper candidate in WWW'19), won the 2022 SIGKDD Dissertation Award Runner-Up

Publications

=== Out of date, please see Google Scholar for details. ===

  • all : conference | journal | pre-print | products & best & top*
  • graph representation learning : graph neural nets | heterogeneous & knowledge graphs | pre-training & self-supervised | network embedding | theory | scalability | data & benchmarks
  • social & information networks : user modeling & profiling | link prediction | recommendation | science of science
  • foundation models : LLMs | ai agent | graph pre-training

    1. Understanding Emergent Abilities of Language Models from the Loss Perspective.
      Zhengxiao Du, Aohan Zeng, Yuxiao Dong, Jie Tang.
      NeurIPS'24.
      pdf
    2. ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search.
      Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, Jie Tang.
      NeurIPS'24.
      pdf
    3. GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment.
      Zhenyu Hou, Haozhan Li, Yukuo Cen, Jie Tang, Yuxiao Dong.
      arXiv:2406.02953 , 2024.
      pdf
    4. ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.
      Team GLM.
      arXiv:2406.12793 , 2024.
      pdf github in ChatGLM for chatglm.cn and Zhipu MaaS!
    5. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment.
      Xiao Liu, Xixuan Song, Yuxiao Dong, Jie Tang.
      arXiv:2404.00604, 2024.
      pdf
    6. ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline.
      Yifan Xu, Xiao Liu, Xinghan Liu, Zhenyu Hou, Yueyan Li, Xiaohan Zhang, Zihan Wang, Aohan Zeng, Zhengxiao Du, Wenyi Zhao, Jie Tang, Yuxiao Dong.
      arXiv:2404.02893 , 2024.
      pdf github in ChatGLM for chatglm.cn and Zhipu MaaS!
    7. ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback.
      Zhenyu Hou, Yilin Niu, Zhengxiao Du, Xiaohan Zhang, Xiao Liu, Aohan Zeng, Qinkai Zheng, Minlie Huang, Hongning Wang, Jie Tang, Yuxiao Dong.
      arXiv:2404.00934 , 2024.
      pdf in ChatGLM for chatglm.cn and Zhipu MaaS!
    8. LongAlign: A Recipe for Long Context Alignment of Large Language Models.
      Yushi Bai, Xin Lv, Jiajie Zhang, Yuze He, Ji Qi, Lei Hou, Jie Tang, Yuxiao Dong, Juanzi Li.
      arXiv:2401.18058 , 2024.
      pdf in ChatGLM for chatglm.cn and Zhipu MaaS!
    9. CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion.
      Wendi Zheng, Jiayan Teng, Zhuoyi Yang, Weihan Wang, Jidong Chen, Xiaotao Gu, Yuxiao Dong, Ming Ding, Jie Tang.
      arXiv:2403.05121, 2024.
      pdf in chatglm.cn and Zhipu MaaS!
    10. APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding.
      Mingdao Liu, Aohan Zeng, Bowen Wang, Peng Zhang, Jie Tang, Yuxiao Dong.
      arXiv:2401.06761 , 2024.
      pdf
    11. AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent.
      Hanyu Lai, Xiao Liu, Iat Long Iong, Shuntian Yao, Yuxuan Chen, Pengbo Shen, Hao Yu, Hanchen Zhang, Xiaohan Zhang, Yuxiao Dong, Jie Tang.
      KDD'24, arXiv:2404.03648.
      pdf github
    12. AgentTuning: Enabling Generalized Agent Abilities for LLMs.
      Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, Jie Tang.
      Findings of ACL'24, arXiv:2310.12823.
      pdf github in ChatGLM for chatglm.cn and Zhipu MaaS!
    13. xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein.
      Bo Chen, Xingyi Cheng, Yangli-ao Geng, Shen Li, Xin Zeng, Boyan Wang, Jing Gong, Chiming Liu, Aohan Zeng, Yuxiao Dong, Jie Tang, Le Song.
      bioRxiv, 2023.
      pdf
    14. AlignBench: Benchmarking Chinese Alignment of Large Language Models.
      Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Zhuoer Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang.
      ACL'24, arXiv:2311.18743.
      pdf github
    15. LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.
      Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li.
      ACL'24, arXiv:2310.12823.
      pdf github
    16. CogAgent: A Visual Language Model for GUI Agents.
      Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxiao Dong, Ming Ding, Jie Tang.
      CVPR'24, Full paper, Poster (Highlight) presentation, arXiv:2312.08914.
      pdf github in chatglm.cn and Zhipu MaaS!
    17. AgentBench: Evaluating LLMs as Agents.
      Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, Jie Tang.
      ICLR'24, arXiv:2308.03688.
      pdf github leaderboard
    18. RecDCL: Dual Contrastive Learning for Recommendation.
      Dan Zhang, Yangliao Geng, Wenwen Gong, Zhongang Qi, Zhiyu Chen, Xing Tang, Ying Shan, Yuxiao Dong, Jie Tang.
      WWW'24.
    19. ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation.
      Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, Yuxiao Dong.
      NeurIPS'23.
      pdf github huggingface
    20. WebGLM: Towards An Efficient Web-enhanced Question Answering System with Human Preference.
      Xiao Liu, Hanyu Lai, Yu Hao, Yifan Xu, Aohan Zeng, Zhengxiao Du, Peng Zhang, Yuxiao Dong, Jie Tang.
      KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
      pdf github huggingface in chatglm.cn and Zhipu MaaS!
    21. CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X.
      Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang.
      KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
      pdf github huggingface blog VS Code JetBrains in CodeGeeX plugins for VS Code, JetBrains, etc.!
    22. BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs.
      Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Zhitao Ying, Yukuo Cen, Yangliao Geng, Jie Tang.
      KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
      pdf code
    23. WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window.
      Yifan Zhu, Cong Fangpeng, Dan Zhang, Wenwen Gong, Qika Lin, wenzheng feng, Yuxiao Dong, Jie Tang.
      KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
    24. GLM-130B: An Open Bilingual Pre-trained Model.
      Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, Jie Tang.
      ICLR'23 ( In Proceedings of the 11th International Conference on Learning Representations), 2023.
      pdf code&model ChatGLM-6B ChatGLM
    25. SketchNE: Embedding Billion-Scale Networks Accurately in One Hour.
      Yuyang Xie, Yuxiao Dong, Jiezhong Qiu, Wenjian Yu, Xu Feng, Jie Tang.
      TKDE'23 (IEEE Transaction on Knowledge and Data Engineering), 2023.
      pdf code
    26. GraphMAE2: A Decoding-enhanced Masked Self-supervised Graph Learner.
      Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang.
      WWW'23 (Proceedings of The Web Conference 2023), 2023.
      pdf code&data
    27. Semi-Supervised Social Bot Detection with Initial Residual Relation Attention Networks.
      Ming Zhou, Wenzheng Feng, Yifan Zhu, Dan Zhang, Yuxiao Dong, and Jie Tang.
      ECML-PKDD'23 (Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), 2023.
      pdf demo best student paper
    28. GraphMAE: Self-Supervised Masked Graph Autoencoders.
      Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang.
      KDD'22 (Proc. of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2022. Full Research Paper.
      pdf code&data
    29. Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries.
      Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, Jie Tang.
      KDD'22 (Proc. of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2022. Full Research Paper.
      pdf code&data
    30. GRAND+: Scalable Graph Random Neural Networks.
      Wenzheng Feng, Yuxiao Dong, Huang Tinglin, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang.
      WWW'22 (Proceedings of The Web Conference 2022), 2022.
      pdf code
    31. SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs.
      Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang.
      WWW'22 (Proceedings of The Web Conference 2022), 2022.
      pdf code best paper candidate
    32. ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs.
      Yanling Wang, Jing Zhang, Haoyang Li, Yuxiao Dong, Hongzhi Yin, Cuiping Li, Hong Chen.
      WWW'22 (Proceedings of The Web Conference 2022), 2022.
      pdf code
    33. Adaptive Diffusion in Graph Neural Networks.
      Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang.
      NeurIPS'21 (Proc. of the 35th Annual Conference on Neural Information Processing Systems), 2021.
      pdf code
    34. OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.
      Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec.
      NeurIPS'21 D&B (Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks), 2021.
      pdf OGB-LSC@KDDCUP 2021 OGB
    35. Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning.
      Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang.
      NeurIPS'21 D&B (Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks), 2021.
      pdf GRB leaderboard
    36. A Large-Scale Database for Graph Representation Learning.
      Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng Chau.
      NeurIPS'21 D&B (Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks), 2021.
      pdf MalNet data
    37. TDGIA: Effective Injection Attacks on Graph Neural Networks
      Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang.
      KDD'21 (Proc. of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2021. Full Research Paper.
      pdf code
    38. Are We Really Making Much Progress? Revisiting, Benchmarking and Refining the Heterogeneous Graph Neural Networks
      Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang.
      KDD'21 (Proc. of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2021. Full Research Paper.
      pdf code&data HGB leaderboard
    39. GPT-GNN: Generative Pre-Training of Graph Neural Networks
      Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun
      KDD'20 (Proc. of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2020. Full Research Paper.
      pdf code bibtex in fb products! 6th most cited paper in KDD'20
    40. GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
      Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang
      KDD'20 (Proc. of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2020. Full Research Paper.
      pdf code bibtex most cited paper in KDD'20
    41. Open Graph Benchmark: Datasets for Machine Learning on Graphs
      Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec
      NeurIPS'20 (Proc. of the 34th Annual Conference on Neural Information Processing Systems), 2020. Spotlight Paper (385/9454).
      pdf OGB leaderboard
    42. Graph Random Neural Networks for Semi-Supervised Learning on Graphs
      Wenzheng Feng=, Jie Zhang=, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang
      NeurIPS'20 (Proc. of the 34th Annual Conference on Neural Information Processing Systems), 2020. Oral Paper (105/9454).
      pdf slides poster code bibtex
    43. Heterogeneous Graph Transformer
      Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun.
      WWW'20 (Proc. of the 2020 Web Conference), short paper, oral.
      pdf data&code (pyG) code (DGL) bibtex in pyG in dgl most cited in WWW'20 in msft & fb products!
    44. Heterogeneous Network Representation Learning
      Yuxiao Dong, Ziniu Hu, Kuansan Wang, Yizhou Sun, Jie Tang.
      IJCAI'20 (Proc. of the 29th International Joint Conference on Artificial Intelligence), 2020.
      pdf slides poster OGB ogbn-mag leaderboard OGB-LSC mag240m leaderboard HGB leaderboard bibtex
    45. NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
      Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang and Jie Tang
      WWW'19 (Proc. of the 2019 Web Conference), 2019. Full paper (Oral).
      pdf code slides poster bibtex best paper candidate in msft products!
    46. ProNE: Fast and Scalable Network Representation Learning
      Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, Ming Ding.
      IJCAI'19 (Proc. of the 28th International Joint Conference on Artificial Intelligence), 2019. Full paper (Oral).
      pdf code bibtex
    47. OAG: Toward Linking Large-scale Heterogeneous Entity Graphs
      Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang
      KDD'19 (Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2019. Full Applied Data Science Paper (Oral), 6.4%.
      pdf code data bibtex
    48. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec.
      Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang.
      WSDM'18 (Proc. of the 11th ACM International Conference on Web Search and Data Mining), 2018. Full paper (Oral), 16%.
      pdf code slides bibtex Microsoft Research Blog 2nd most cited paper in WSDM'18
    49. metapath2vec: Scalable Representation Learning for Heterogeneous Networks.
      Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami.
      KDD'17 (Proc. of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2017. Full Research Paper (Oral), 8.5%.
      pdf data&code slides poster video bibtex in dgl most cited paper in KDD'17 in msft products!

    Teaching & Tutorials

    1. 2024--, Advanced Articifical Intelligence (Graduate) --- with Prof. Yang Liu, CS, Tsinghua
    2. 2023--, Numerical Analysis (Undergraduate), CS, Tsinghua
    3. 2023--, Programming (Undergraduate), CS, Tsinghua
    4. 2023/2024, Guest Lecture, LLMs, Tsinghua EMBA
    5. 2023, Guest Lecture, LLMs, Peking EMBA
    6. 2022/2023, Guest Lecture, Data Mining, CS, Tsinghua
    7. 2020, Guest Lecture, Knowledge Graph, Computer Science Department, Stanford University
    8. 2020/2021/2022, Guest Lecture, Advanced Machine Learning, Computer Science Department, Tsinghua University
    9. 2023, Tutorial, Graph Representation and Pre-Training, WWW'23
    10. 2020, Tutorial (Invited), Graph Representation Learning, ECML/PKDD'20 slides
    11. 2019, Tutorial, Representation Learning on Networks (full day), WWW'19 slides
    12. 2019, Tutorial, Learning from Networks (full day), KDD'19 slides
    13. 2018, Tutorial, Computational Models for Social and Information Network Analysis, KDD'18 slides

    Invited Talks

    1. 2024: Invited talk at Stanford, CA
    2. 2024: Invited talk at Nanjing U.
    3. 2023: Invited talk at Caltech, CA
    4. 2023: Keynote at IJCAI'23 LLM Symposium: The GLM Large Language Models: From GLM-130B to ChatGLM to AgentBench
    5. 2023: Invited Talk at KDD China Summit 2023: Graph Pre-Training: From GPT-GNN to GraphMAE to GCC
    6. 2022: IJCAI Early Career Spotlight Invited Talk
    7. 2019: Invited Talk at INFORMS'19 Informs Annual Meeting: Representation Learning on Networks
    8. 2019: Microsoft Security and Compliance AI Summit
    9. 2019: AI and Tensor Conference at Los Alomos National Lab
    10. 2019: Invited Talk at NetSci'19 Satellite on Quantifying Success
    11. 2019: Invited Talk at NetSci'19 Satellite on Network Representation Learning
    12. 2018: Invited Talk at NetSci'18 Higher-Order Models in Network Science Satellite (HONS'18)
    13. 2018: Invited Talk at NICO, Northwestern University, IL
    14. 2017: Invited Talk at Labs in Tsinghua University
    15. 2016: Keynote at ACM JCDL'16 Workshop on Mining Scientific Publications (WOSP'16)
    16. 2016: Invited Talks at Labs in Stanford University / Tsinghua University / Chinese Academy of Sciences (CAS)
    17. 2015: Invited Talks at Labs in Oxford University / Hesburgh Library at University of Notre Dame
    18. 2023: Invited talk at Annual Meetings of CISP, Tsinghua Foundation Model Research Center
    19. 2023: Invited talks at LLM-related Forums at WAIC'23, BDTC'23, CSIG'23
    20. 2023: Invited Talk at CCF ADL / CCF SSP
    21. 2023: Invited Talk at Tencent / Webank / CMCC / VMware China / iTechClub / OpenAtom Global Open-Source Summit
    22. 2023: Invited Talk at Tsinghua School of Management, Tsinghua PBC School of Finance / ICT of CAS / Beijing Normal University
    23. 2022: Invited Talk at Shandong University / Renmin University of China / Beijing Jiaotong University
    24. 2022: Invited Talk at CNCC 2022 LLM Forum
    25. 2021: Invited Talk at Tsinghua University, Computer Science Department
    26. 2020: Invited Talk at Beijing BAAI Conference Knowledge & Intelligence Forum: Graph Representation Learning and Pre-Training
    27. 2020: Invited Talk at CCF Young Elite Forum
    28. 2019: Invited Talk at Tsinghua University, Computer Science Department

    Professional Services

    Conference Organizers:

    1. Generative AI Day Co-Chair of KDD'24
    2. Large Language Model (LLM) Day Co-Chair of ACM WWW'24
    3. Large Language Model (LLM) Day Co-Chair of ACM KDD'23
    4. Track Co-Chair of WWW'23 Social Network Analysis and Graph Algorithms Track
    5. Ph.D. Symposium Co-Chair of the 2023 Web Conference (WWW'23)
    6. Program Co-Chair of ECML/PKDD'21 Applied Data Science Track
    7. Program Co-Chair of ACM/IEEE ASONAM'21 Industry Track
    8. Program Co-Chair of ECML/PKDD'20 Applied Data Science Track
    9. Program Co-Chair of National Conference on Social Media Processing (SMP'20)
    10. Workshop Co-Chair of SIAM SDM'20
    11. Deep Learning Day Co-Chair of ACM KDD'20
    12. Deep Learning Day Co-Chair of ACM KDD'19
    13. Deep Learning Day Co-Chair of ACM KDD'18

    Journal Editors:

    1. Associate Editor of IEEE Transactions on Big Data (TBD), 2020--
    2. Associate Editor of Springer Social Network Analysis and Mining (SNAM), 2021--
    3. Associate Editor of AI OPEN, 2020--
    4. Guest Editor of Special Issue "AI for COVID-19" at IEEE TBD, 2020

    Conference PC members:

    1. 2026: ICLR (Area Chair), NeurIPS (Area Chair), KDD (Area Chair)
    2. 2025: ACL (Senior Area Chair), ICLR (Area Chair), KDD (Area Chair), WWW (Area Chair), AAAI (Senior PC)
    3. 2024: ICLR (Area Chair), WWW (Area Chair), AAAI (Senior PC)
    4. 2023: KDD (Senior PC), WWW (Track Co-Chair), AAAI (Senior PC), ECML-PKDD (Area Chair)
    5. 2022: KDD (Senior PC), WWW (Senior PC), AAAI (Senior PC), ECML/PKDD (Area Chair)
    6. 2021: KDD, NeurIPS, AAAI (Senior PC), ECML/PKDD (PC Co-Chair ADS), ASONAM (Industry Track Co-Chair)
    7. 2020: KDD, NeurIPS, WSDM, WWW, SDM, ECML/PKDD (PC Co-Chair ADS), SMP (PC Co-Chair)
    8. 2019: KDD, WSDM, WWW, SDM, ICDM, CIKM, AAAI
    9. 2018: KDD, WSDM, WWW, SDM, ICDM, ECML/PKDD, DSAA
    10. 2017: KDD, WSDM, WWW, SDM, ASONAM, CIKM
    11. 2016: ASONAM, CIKM
    12. 2015: ASONAM

    Competition Organizers:

    1. NeurIPS Competition OGB-LSC 2022: A Large-Scale Challenge for ML on Graphs at NeurIPS'22
    2. KDD CUP OGB-LGC: A Large-Scale Challenge for Machine Learning on Graphs at KDD'21

    Journal Reviewers:

    1. Nature Machine Intelligence
    2. Nature Human Behavior
    3. Nature Scientific Reports
    4. JMLR, Journal of Machine Learning Research
    5. CSUR, ACM Computing Surveys
    6. TKDD, ACM Transactions on the Knowledge Discovery from Data
    7. TWEB, ACM Transactions on the Web
    8. TKDE, IEEE Transactions on Knowledge and Data Engineering
    9. TMC, IEEE Transactions on Mobile Computing
    10. TBD, IEEE Transactions on Big Data

    Contact Info

    1-309, FIT Building
    Tsinghua University
    Beijing 100084, China

    Twitter/X: @ericdongyx

    yuxiaod@@tsinghua.edu.cn
    ericdongyx@gmail.com

    *All stats are observed from Google Scholar on Jun. 2022