Yu Rong (荣钰)

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Hi! I am currently the head of Language And Science Lab (LASA) of DAMO Academy of Alibaba Group. I am generally interested in developing the novel and efficient models with various complex data structures, includeing graphs and sequences to tackle real-world challenges, particularly in the realm of Language and Science.

Prior joining DAMO Academy, I served as a principal researcher at Tencent AI Lab for seven years. I briefly served as a Postdoctoral Research Fellow at The Chinese University of Hong Kong. I received Ph.D degree at The Chinese University of Hong Kong in 2016, under the supervision of Professor Hong CHENG. Before that I obtained my B.S. degree with honors from Sun Yat-sen University.

JOB OPENINGS NOW!!: I am looking for highly motivated full-time positions / research interns on large language model and AI for Science.

🔥 What's New

Nov, 2024 One paper is accepted by ICDE 2025!
Nov, 2024 One paper about sustainable battery recycling is published by Nature Communications! Check out this Link
Nov, 2024 One paper for the protein desgin is accepted by KDD 2025! Check out our project page: PAAG
Aug, 2024 The campus recruitment of Damo Academy has started, offering four positions related to AI4Sci.
Aug, 2024 I will serve as Area Chair for ICLR 2025.

🔍 Current Research Topics

  • Deep Graph Learning
    • Foundations of Graph Neural Network
  • Large Language Models
  • AI for Science
    • AI for Drug Discovery
    • Physical Dynamic Simulation

📝 Recent Publications

2024

  1. KDD
    Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Broad Physical Dynamics Learning
    Zinan Zheng, Yang Liu, Jia Li, Jianhua Yao, and Yu Rong
    In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
  2. ICLR
    SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
    Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, and 2 more authors
    In The Twelfth International Conference on Learning Representations, 2024
  3. VLDB
    Inductive Attributed Community Search: To Learn Communities Across Graphs
    Shuheng Fang, Kangfei Zhao, Yu Rong, Zhixun Li, and Jeffrey Xu Yu
    Proc. VLDB Endow., Aug 2024
  4. ICLR
    Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel
    Xuan Li, Zhanke Zhou, Jiangchao Yao, Yu Rong, Lu Zhang, and Bo Han
    In The Twelfth International Conference on Learning Representations, 2024
  5. Nature Methods
    scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding
    Wei Li, Fan Yang, Fang Wang, Yu Rong, Linjing Liu, Bingzhe Wu, and 2 more authors
    Nature Methods, Apr 2024

2023

  1. Nature Comm.
    Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning
    Shengyu Tao, Haizhou Liu, Chongbo Sun, Haocheng Ji, Guanjun Ji, Zhiyuan Han, and 11 more authors
    Nature Communications, Dec 2023
  2. KDD
    Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation
    Zeyu Cao, Zhipeng Liang, Bingzhe Wu, Shu Zhang, Hangyu Li, Ouyang Wen, and 2 more authors
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
  3. CIKM
    Geometric Graph Learning for Protein Mutation Effect Prediction
    Kangfei Zhao, Yu Rong, Biaobin Jiang, Jianheng Tang, Hengtong Zhang, Jeffrey Xu Yu, and 1 more author
    In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023
  4. NeurIPS
    Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
    Liming Wu, Zhichao Hou, Jirui Yuan, Yu Rong, and Wenbing Huang
    In Advances in Neural Information Processing Systems, 2023

💬 Talks

🎖Honors and Awards

  • 2022, Champion of 2nd Open Catalyst Challenge, NeurIPS
  • 2020, 10% of High-scoring Reviewers, NeurIPS
  • 2017, Research Fellowship Scheme, CUHK
  • 2012, Outstanding Undergraduate Thesis Award, SYSU
  • 2012, National Undergraduate Scholarship, MoE
  • 2011, Google Excellence Scholarship, Google