Lecheng Zheng

My name is Lecheng Zheng (郑乐成). I am currently a Ph.D. student at the Department of Computer Science, University of Illinois at Urbana–Champaign . I work as a research assistant at iSAIL Lab under the supervision of Prof. Jingrui He . Before that, I got my Bachelor's degree in Computer Science from the University of Kansas advised by Prof. Jun(Luke) Huan .

My research primarily centers on enhancing the trustworthiness and efficiency of machine learning algorithms across various modalities and disciplines, with the ultimate goal of making ML models more accessible and inclusive. My research interests covers a wide range of topics:

  • Heterogeneity: multi-modal fusion, multi-label/multi-task learning;
  • Generation: Missing value imputation, graph generation;
  • Trustworthy: bias, fairness, robustness, and transferability;
  • Graph Mining: graph anomaly detection, graph oversmoothing;
  • Applications: sustainability, climate, security, finance.
Feel free to drop me an e-mail, if you are interested in my research and want to discuss relevant research topic or potential collaborations!

CV  /  Email  /  Google Scholar  /  Github  /  Linkedin  

Education

Ph.D.          2019 - 2025 (expected)
                       University of Illinois Urbana-Champaign (UIUC)
                       Department of Computer Science
                       Advisor: Prof. Jingrui He

B.S.              2012 - 2016
                       University of Kansas
                       Department of Electrical Engineering and Computer Science
                       Advisor: Prof. Luke Huan
Work Experience

NEC Search, Princeton, NJ
Research Intern • May. 2023 - Aug. 2023
Multi-modal Root Cause Analysis
One paper published at WWW'24 (Oral). Two in submission.
Main Advisors: Dr. Zhengzhang Chen and Dr. Haifeng Chen at NEC.
News
Oct, 2024 One paper "DrGNN: Deep Residual Graph Neural Network with Contrastive Learning" accepted @ TMLR'24.
Sep, 2024 One paper "Multi-label Sequential Sentence Classification via Large Language Model" accepted @ EMNLP’24.
May, 2024 One paper Heterogeneous Contrastive Learning for Foundation Models and Beyond accepted @ KDD’24.
Jan, 2024 One paper MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems accepted @ WWW’24.
Nov, 2023 One paper FairGen: Towards Fair Graph Generation accepted @ ICDE’24.
Dec, 2023 One paper Fairness-aware multi-view clustering accepted @ SDM’23.
Sep, 2022 One paper MentorGNN: Deriving Curriculum for Pre-Training GNNs accepted @ CIKM’22.
May, 2022 One paper Contrastive learning with complex heterogeneity accepted @ KDD’22.
Dec, 2021 One paper Outlier impact characterization for time series data accepted @ AAAI’22.
Jan, 2021 One paper Deep co-attention network for multi-view subspace learning accepted @ WWW’21.
Publications (*equal contribution)
       Conference
              Journal

              Preprint

Tutorial

  • Heterogeneous Contrastive Learning for Foundation Models and Beyond

    Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, Jingrui He

    KDD 2024. WebsiteSlides

  • Contrastive Learning: A Heterogeneous Perspective

    Lecheng Zheng, Jingrui He

    SDM 2023. Website

  • Exploring Rare Categories on Graphs: Representation, Inference, and Generalization

    Dawei Zhou, Lecheng Zheng, Jingrui He

    IJCAI 2020. Website

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    Services

    Program Committee Members
    • 2025: AAAI, ICLR, SDM, The WebConf, AISTATS, WSDM
    • 2024: ICML, IJCAI, ICLR, WSDM, AAAI, SDM, The WebConf, PAKDD, NeurIPS, CIKM, BigData
    • 2023: IJCAI, WSDM, AAAI, SDM, The WebConf, SIGKDD, NeurIPS, BigData
    • 2022: The WebConf, SDM, AAAI, IJCAI, SIGKDD, CIKM
    • 2021: CIKM, IJCAI, AAAI
    Journal Reviewer
    • Data Mining and Knowledge Discovery
    • Transactions on Knowledge Discovery from Data
    • Transactions on Knowledge and Data Engineering