Dongsheng Luo (骆东升)

Assistant Professor
Knight Foundation School of Computing and Information Sciences
Florida International University
Email: x@fiu.edu where x = dluo
Office: CASE 212 B.

Linkedin, Github, Google Scholar


Short bio

I am an assistant professor at Florida International University. I obtained my doctoral degree at the College of Information Science and Technology, Pennsylvania State University, supervised by Prof. Xiang Zhang. I obtained my B.Eng degree in computer science and technology from Beihang University in 2017, supervised by Prof. Shuai Ma. My research focuses on data mining, deep learning, and natural language processing.

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I have several positions available for research interns (flexible time) and several fully-funded PhD positions (Spring and Fall 2023) available. If you are interested in working with me, please feel free to drop me an email with your CV.

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Research Interests

My research bridges the areas of artificial intelligence, data mining, machine learning, and natural language processing. I am particularly interested in developing robust, integrative, and explainable tools that extract clean and human-understandable knowledge from noisy and heterogeneous network data, focusing on but not limited to application fields of neuroscience, system management, and biomedicine.

  • Robust learning on noisy data
  • Integrative methods for heterogeneous data
  • Contrastive Learning
  • Applications of graph algorithms
  • Updates

  • 11/2022: A paper "TopoImb" was accepted to Learning on Graphs Conference 22.
  • 11/2022: A paper "InfoTS" was accepted to AAAI 23.
  • 11/2022: A paper on Random Walk was accepted to TKDE 22.
  • 10/2022: A paper on explainable GNNs was accepted to WSDM 23.
  • 08/2022: A paper, FedMN, was accepted to ICDM 22.
  • 08/2022: I joined FIU as an Assistant Professor
  • 10/2021: Invited to give a talk at Microsoft Search, Assistant and Intelligence. Thanks for Kunho's Invitation.
  • 09/2021: One paper, InfoGL, was accepted to NeurIPS 2021.
  • 03/2021: Honor to receive College of IST Ph.D. Award for Research Excellence
  • 01/2021: TKDE extended abstract, NDCC, was accepted to ICDE 2021.
  • 12/2020: One paper, ASR, was accepted to DLG-AAAI 2021 workshop.
  • 12/2020: I obtained my M.S. degree.
  • 11/2020:Invited to serve as a PC member for KDD'21.
  • 10/2020: One paper, PTDNet, was accepted to WSDM'21.
  • 10/2020: Passed the M.S. thesis defense.
  • 09/2020: One paper, PGExplainer, was accepted to NeurIPS'20.
  • 08/2020: Invited to serve as a PC member for AAAI'21.
  • 05/2020:Happy to join NEC lab as a research intern working with Dr. Wei Cheng.
  • 05/2020:One paper, RWM, was accepted to SIGKDD'20 (Research Track).
  • 05/2020:One paper, NDCC, was accepted to TKDE.
  • 04/2020:Invited to serve as a PC member for KDD'20.
  • 10/2019: One paper, DMGC, was accepted to WSDM'20.
  • Publications

    Time Series Contrastive Learning with Information-Aware Augmentations
    Dongsheng Luo , W. Cheng, Y. Wang, D. Xu, J. Ni, W. Yu, X. Zhang, Y. Liu, Y. Chen, H. Chen, Xiang Zhang
    to appear In AAAI Conference on Artificial Intelligence (AAAI), 2023

    Towards Faithful and Consistent Explanations for Graph Neural Networks
    Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang.
    to appear In ACM International Conference on Web Search and Data Mining (WSDM), 2023.

    TopoImb: Toward Topology-level Imbalance in Learning from Graphs
    Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang.
    to appear In Learning on Graphs Conference (LOG), 2022.

    Random Walk on Multiple Networks.
    Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiong Yu, Jun Huan, Xiao Liu, Xiang Zhang
    to appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.

    A Collective Approach to Scholar Name Disambiguation. [pdf] [code][demo][slides][poster]
    Dongsheng Luo, Shuai Ma, Yaowei Yan, Chunming Hu, Xiang Zhang, and Jinpeng Huai
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.

    Personalized Federated Learning via Heterogeneous Modular Networks.
    Tianchun Wang, Wei Cheng, Dongsheng Luo, Wenchao Yu, Jingchao Ni, Liang Tong, Haifeng Chen, Xiang Zhang.
    In 2022 IEEE International Conference on Data Mining (ICDM'22).

    InfoGCL: Information-Aware Graph Contrastive Learning
    Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang
    The 35th Conference on Neural Information Processing Systems (NeurIPS), 2021

    A Collective Approach to Scholar Name Disambiguation. [pdf][code][slides][poster]
    Dongsheng Luo, Shuai Ma, Yaowei Yan, Chunming Hu, Xiang Zhang, and Jinpeng Huai
    the IEEE International Conference on Data Engineering (ICDE), 2021 (TKDE Extended Abstract)

    Deep Multi-Instance Contrastive Learning with Dual Attention for Anomaly Precursor Detection
    Dongkuan Xu, Wei Cheng, Jingchao Ni, Dongsheng Luo, Masanao Natsumeda, Dongjin Song, Bo Zong, Haifeng Chen, Xiang Zhang
    The 21th SIAM International Conference on Data Mining (SDM), 2021

    Attentive Social Recommendation:Towards User And Item Diversities [arxiv][code]
    Dongsheng Luo, Yuchen Bian, Xiang Zhang, Jun Huan
    DLG-AAAI21 Workshop, 2021

    Learning to Drop: Robust Graph Neural Network via Topological Denoising [arxiv][code] [slides][poster][video] [中文解读]
    Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, Xiang Zhang
    In Proceedings of 14th ACM International Conference on Web Search and Data Mining (WSDM), 2021

    Parameterized Explainer for Graph Neural Network [arxiv][appendix][code&data] [slides][poster][video] [ML Reproducibility 1] [ML Reproducibility 2] [中文解读]
    Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang
    in Proceedings of 34th Conference on Neural Information Processing Systems (NeurIPS), 2020

    Local Community Detection in Multiple Networks [pdf] [appendix] [code&data][data(DBLP)][slides][video] [中文解读]
    Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiao Liu, Jun Huan, Xiang Zhang
    in Proceedings of 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD),2020

    Deep Multi-Graph Clustering via Attentive Cross-Graph Association [pdf][poster] [spotlight][code&data]
    Dongsheng Luo, Jingchao Ni, Suhang Wang, Yuchen Bian, Xiong Yu and Xiang Zhang
    Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2020

    Memory-Based Random Walk for Multi-Query Local Community Detection. [repo] [code&data]
    Yuchen Bian, Dongsheng Luo, Yaowei Yan, Wei Cheng, Wei Wang, and Xiang Zhang
    Knowledge and Information Systems (KAIS), 2019 (Extended version of the ICDM'2018 paper)

    Constrained Local Graph Clustering by Colored Random Walk
    Yaowei Yan, Yuchen Bian, Dongsheng Luo, Dongwon Lee, and Xiang Zhang
    Proceedings of the International Conference on World Wide Web (WWW), 2019

    Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs. [中文解读]
    Dongkuan Xu, Wei Cheng, Dongsheng Luo, Xiao Liu, Xiang Zhang
    Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2019

    Adaptive Neural Network for Node Classification in Dynamic Networks
    Dongkuan Xu, Wei Cheng, Dongsheng Luo, Yameng Gu, Xiao Liu, Jingchao Ni, Bo Zong, Haifeng Chen, Xiang Zhang.
    Proceedings of the IEEE International Conference on Data Mining (ICDM), 2019

    On Multi-Query Local Community Detection. [repo] [code&data]
    Yuchen Bian, Yaowei Yan, Wei Cheng, Wei Wang, Dongsheng Luo, and Xiang Zhang
    Proceedings of the IEEE International Conference on Data Mining (ICDM), 2018 (Best Paper Candidate)

    Query Independent Scholarly Article Ranking
    Shuai Ma, Chen Gong, Renjun Hu, Dongsheng Luo, Chunming Hu, and Jinpeng Huai
    Proceedings of the IEEE International Conference on Data Engineering (ICDE), 2018

    Local Graph Clustering by Multi-Network Random Walk with Restart [code]
    Yaowei Yan, Dongsheng Luo, Jingchao Ni, Hongliang Fei, Wei Fan, Xiong Yu, John Yen, and Xiang Zhang
    Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018

    Ensemble Enabled Weighted PageRank[pdf]
    Dongsheng Luo, Chen Gong, Renjun Hu, Liang Duan, and Shuai Ma
    The WSDM Cup 2016 - Entity Ranking Challenge, San Francisco, CA, USA, 2016. (The 2nd place in the final ranking)

    Teaching

    FIU
    CAP 6778 Advanced Topics in Data Mining (Fall 2022)

    Teaching Assistant @ PSU
    DS 220-001 Data Management for Data Science (Spring 2021)
    IST 210: Organization of Data (Fall 2020, Fall 2019)
    IST 558: Data Mining II (Spring 2020)
    IST 242: Intermediate & Object-Oriented Application Development (Spring 2019)

    Patents

  • S. Ma, D. Luo, C. Gong, R. Hu, ``an ensemble-based system and method for scholarly article ranking'', Patent Number: CN105740386B.4, China, 2016.
  • W. Cheng, H. Chen, J. Ni, D. Luo, ``Unsupervised Document Representation Learning via Contrastive Augmentation'',U.S., 2021.
  • Professional Talks

  • Denoising and Integrating Complex Networks for Big Data Analysis, Microsoft Search, Assistant and Intelligence, Redmond, Washington, USA, 2021/10
  • Contrastive Time Series Representation Learning via Meta-Learning, NEC Laboratories America, Princeton, USA, 2021/08
  • A Collective Approach to Scholar Name Disambiguation, in ICDE, 2021/04
  • Attentive Social Recommendation: Towards User And Item Diversities, in DLG-AAAI, 2021/02
  • Learning to Drop: Robust Graph Neural Network via Topological Denoising, in WSDM, 2021/02
  • Parameterized Explainer for Graph Neural Network, in NeurIPS, 2020/12
  • Unsupervised Document Representation Learning via Contrastive Augmentation, NEC Laboratories America, Princeton, USA, 2020/08
  • Local Community Detection in Multiple Networks, in SIGKDD, 2020/08
  • Deep Multi-Graph Clustering via Attentive Cross-Graph Association, in WSDM, Houston, USA, 2020/02
  • Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs, in IJCAI, Macau, China, 2019/08
  • Mentoring

    Tianxiang Zhao (Summer 2021 -), a Ph.D. student at IST, PSU.
  • Research topic: Explainable learning on graph neural networks; transfer learning
  • Tianchun Wang (Spring 2021 -), a Ph.D. student at IST, PSU.
  • Research topic: Robust learning on graph neural networks; Dynamic Networks
  • Xinnan Dai (Summer 2021 -), a M.S. student at ShanghaiTech University.
  • Research topic: Disentangled explainable learning on graph neural networks
  • Wenhan Deng (May 2020-Aug 2020), Undergraduate student at University of Michigan, Ann Arbor, now a M.S. student at Northwestern University.
  • Research topic: Research topic: On the oversmoothing of graph neural networks.
  • Yingheng Wang (Jan.2019 -Feb. 2019), Undergraduate student at Tongji University, now a Ph.D. student at Cornell University.
  • Research topic: Integrative learning on multi-domain networks; Time series data analysis
  • Professional Services

    Senior PC Member:
    AAAI 23
    PC Member & Reviewer:
    KDD 20, 21, 22
    NeurIPS 22
    AAAI'21
    SDM'22
    ICDM'22
    WSDM'23

    Journal Reviewer:
    VLDB Journal
    IEEE Transactions on Knowledge and Data Engineering (TKDE)
    Knowledge and Information Systems (KAIS)
    Data Mining and Knowledge Discovery (DMKD)
    IEEE Transactions on Big Data (TBD)
    IEEE Computational Intelligence Magazine
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
    Neurocomputing
    Sensors

    External Conference Reviewer:
    KDD: 18, 19, 20, 21
    AAAI: 19, 20, 21
    WWW: 20, 21, 22
    WSDM: 19, 20, 21
    ICDM: 18, 19
    SDM: 18, 19, 20, 21, 22
    CIKM'19

    Honors and Awards

    IST Travel Award, 2021
    College of IST Ph.D. Award for Research Excellence, Penn State University, 2021
    SIGIR Travel Award, 2021
    KDD Registration Award, 2020
    IST Travel Award, 2020
    Best Paper Award Candidate, ICDM, 2018
    Outstanding Undergraduate Student Award of Beijing City, 2017
    CSC Outstanding Undergraduate Exchange Program Award, 2016
    WSDM Cup 2016 – Entity Ranking Challenge the 2nd place in the final ranking, 2016
    China National Undergraduate Scholarship: 2014, 2015


    *Last updated on 11/08/2022*