About

I am a Research Scientist in Nanyang Technological University (NTU) worked with Prof. Han Yu. I am also appointed as an Honorary Postdoctoral Research Fellow at Chinese University of Hong Kong where I received my Ph.D. degree advised by Prof. Irwin King.

My research interest is broadly Representation Learning, focusing on extracting meaningful representation from various data types, including unlabeled, noisy, adversarial, and graph data. Specifically, I recently focus on theoretical and algorithmic approaches for Large Language Models (Self-Supervised Learning, Parameter Efficient Fintuining), Safety of Foundation Models (Federated, Watermarking), and Graph Learning.

Prior to my Ph.D., I have also worked as Senior Applied Machine Learning Engineer at Alibaba Group and JD.com.

News

  • 2024 Oct: Appointed as a joint researcher at Alibaba-NTU Global e-Sustainability CorpLab (ANGEL)
  • 2024 Aug: Join the Nanyang Technological University as the Research Scientist
  • 2024 May: One KDD'24 paper has been accepted. It is honor to have first-authorded paper being accepted to KDD in three consecutive years
  • 2024 Apirl: It is thrilled to announce that I have passed the defense for my Ph.D. degree Congratulations! Dr. Zhang!
  • 2024 Apirl: Another Survey on Semi-supervised Federatd Learning has been accepted to IJCAI survey Track (48/232). Congratulations to Zixing!
  • 2024 Mar: Our Survey on Trustworthy Federated Learning has been accept to ACM Transactions on Intelligent Systems and Technology (TIST)
  • 2024 Jan: One papers has been accepted as Finding in the NAACL'24. Congratulations to Zhihan!
  • 2023 Nov: I was invited by Prof. Kun Kuang to give a talk on Graph Self-supervised Learning at ZJU
  • 2023 Sep: Three papers has been accepted in the NeurIPS'23. Two of them are mark as Spotlight
  • 2023 May: One papers has been accepted in the KDD'23
  • 2023 Mar: One papers has been accepted in the SIGIR'23
  • 2023 Feb: One papers has been accepted in the WWW'23
  • 2022 Nov: Two papers has been accepted in the AAAI'23
  • 2022 July: I was invited to give a talk on graph representation learning at CVR Group leaded by Prof. Yuchao Dai
  • 2022 May: Two papers has been accepted in the KDD'22
  • 2021 Dec: Our paper "Graph-adpative Rectified Linear Unit for Graph Neural Networks" has been accepted in the WWW'22
  • 2021 April: Our paper "Semi-supervised Multi-label Learning for Graph-structured Data" has been accepted in the CIKM'21
  • 2020 Aug: Left Alibaba Group and start Ph.D. at The Chinese Univeristy of HongKong

Publication

Preprints and Workshop

  • (In reviewing) Recent Advances of Multimodal Continual Learning: A Comprehensive Survey. [PDF]
    Yu, D., Zhang, X., Chen, Y., Liu, A.,Zhang, Y., Yu, P.S. and King, I
    IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • (In reviewing) Can Watermarked LLMs be identified by Users via Crafted Prompts [PDF]
    Liu, A., Guan, S., Liu, Y., Pan, L., Zhang, Y., Fang, L., Wen, L., Yu, P.S. and Hu, X.
    The International Conference on Learning Representations.
  • (IJCAI'19) Additively Homomorphic Encryption-based Deep Neural Network for Asymmetrically Collaborative Machine Learning. [PDF]
    Zhang, Y. and Zhu, H.
    In IJCAI 2019 Workshop on Federated Learning. Solutions have been included in FATE , an industry level open source library for federated learning. See this for detial.

Journals

  • (ACM TIST) A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges
    Zhang, Y., Zeng D., Luo J., , Fu X., ,Xu Z., and King, I
    In ACM Transactions on Intelligent Systems and Technology.
  • (TKDE) Towards Effective Top-N Hamming Search viaBipartite Graph Contrastive Hashing
    Chen, Y., Fang, Y., Zhang, Y., Ma, C., Hong, Y. and King, I.,
    IEEE Transactions on Knowledge and Data Engineering.

Conference

  • (KDD'24) Geometric View of Soft Decorrelation in Self-Supervised Learning[PDF][CODE][BLOG]
    Zhang, Y., Zhu, H., Chen, Y., Song, Z., Fu. X, Meng, Z., Koniusz, P., and King, I.
    In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
  • (IJCAI'24, Survey Track) A Systematic Survey on Federated Semi-supervised Learning[PDF][CODE][BLOG]
    Song, Z., Yang, X.,Zhang, Y., Fu X., Xu, Z., and King, I.
    In 2024 International Joint Conference on Artificial Intelligence.
  • (NAACL'24, Findings) Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering[PDF][CODE][BLOG]
    Guo, Z., Zhang, Y., Zhang, Z., Xu, Z., and King, I.
    In 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics.
  • (NeurIPS'23, Spotlight) Mitigating the Popularity Bias in Graph-based Collaborative Filtering[PDF][CODE][BLOG]
    Zhang, Y., Zhu, H., Chen, Y., Song, Z., Koniusz, P. and King, I.
    In Conference on Neural Information Processing Systems.
  • (NeurIPS'23, Spotlight) No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning[PDF][CODE][BLOG]
    Song, Z., Zhang, Y., and King, I.
    In Conference on Neural Information Processing Systems.
  • (NeurIPS'23) Optimal Block-wise Asymmetric Graph Construction for Semi-supervised Learning.[PDF][CODE][BLOG]
    Song, Z., Zhang, Y., and King, I.
    In Conference on Neural Information Processing Systems.
  • (KDD'23) Contrastive Cross-scale Graph Knowledge Synergy.[PDF][CODE][BLOG]
    Zhang, Y., Chen, Y., Song, Z. and King, I.
    In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
  • (SIGIR'23) BWSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering.[PDF]
    Chen, Y., Zhang, Y.,Song. Z., Yang. M., Chen. M., King, I.
    In International ACM SIGIR Conference on Research and Development in Information Retrieval..
  • (WWW'23) Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space.[PDF]
    Chen, Y., Fang, Y Zhang, Y., King, I.
    In Proceedings of The Web Conference 2023.
  • (AAAI'23, Oral) SFA: Spectral Feature Augmentation for Graph Contrastive Learning.[PDF][CODE][BLOG]
    Zhang, Y., Zhu, H., Song, Z., Koniusz, P. and King, I.
    In AAAI Conference on Artificial Intelligence.
  • (AAAI'23) Graph Component Contrastive Learning for Concept Relatedness Estimation[PDF]
    Ma, Y., Song, Z., Hu, X., Li, J. Zhang, Y. and King, I.
    In AAAI Conference on Artificial Intelligence.
  • (KDD'22) COSTA: Covariance-Preserved Feature Augmentation for Graph Contrastive Learning.[PDF][CODE][BLOG]
    Zhang, Y., Zhu, H., Song, Z., Koniusz, P. and King, I.
    In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
  • (KDD'22) Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification.[PDF]
    Song, Z., Zhang, Y., and King, I.
    In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
  • (WWW'22) Graph-adpative Rectified Linear Unit for Graph Neural Networks.[PDF][CODE][BLOG]
    Zhang, Y., Zhu, H., Song, Z., Koniusz, P. and King, I.
    In Proceedings of The Web Conference 2022.
  • (CIKM'21) Semi-supervised Multi-label Learning for Graph-structured Data.[PDF]
    Song, Z., Meng, Z., Zhang, Y., & King, I.
    In Proceedings of International Conference on Information & Knowledge Management.
  • (ICASSP'20) Discrete Wasserstein Autoencoders for Document Retrieval.[PDF]
    Zhang, Y., Zhu, H.
    In 2020 IEEE International Conference on Acoustics, Speech and Signal Processing.
  • (NAACL'20) .Doc2hash: Learning Discrete Latent variables for Documents Retrieval.[PDF]
    Zhang, Y., Zhu, H.
    In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
  • (ICWSM'20) #DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 US Presidential Debate.[PDF]
    Rizoiu, M. A., Graham, T., Zhang, R., Zhang, Y., Ackland, R., Xie, L.
    In Twelfth International AAAI Conference on Web and Social Media.

Experience

  • 2019.05 - 2020.08, Senior Applied Machine Learning Engineer (Full-Time), Alibaba Group, Hangzhou, China.
  • 2018.07 - 2019.05, Applied Machine Learning Engineer (Full-Time), JD.com, Beijing, China
  • 2016.07 - 2017.02, Research Intern, Data61, Canberra, Australian

Selected Honors and Awards

  • Hong Kong Postgraduate Studentships Award (CUHK), Autumn 2020
  • CECS Dean’s List(ANU), Autumn 2018
  • Notional Scholarship Award (ZZU), Autumn 2015

Teaching

At CUHK, I work as a teaching assistant for the following undergraduate courses:
  • CSCI3150: Computer Science and Society, Spring 2022
  • CSCI5650: Graph Neural Networks (Graduated-Level Course), Autumn 2021
  • CSCI3150: Computer Science and Society, Spring 2021
  • CSCI1130: Introduction to Computing Using Java, Autumn 2022