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!
I am seeking a faculty position in academia now. I would be delighted to discuss how my research and teaching align with the department's vision. Please don't hesitate to reach out for further discussions.
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Email  / 
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Linkedin  
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Education
Ph.D.          2019 - 2025 (expected)
                       University of Illinois Urbana-Champaign (UIUC)
                       Department of Computer Science
                       Advisor: Prof. Jingrui He
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B.S.              2012 - 2016
                       University of Kansas
                       Department of Electrical Engineering and Computer Science
                       Advisor: Prof. Luke Huan
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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.
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News
Jan, 2025 |
Invited Talk @ Virginia Tech regarding "Heterogeneous Machine Learning: Characterization, Generation and Comprehension".
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Dec, 2024 |
Invited Talk @ NEC Laboratory regarding "Heterogeneous Machine Learning: Characterization, Generation and Comprehension".
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Dec, 2024 |
Guest Lecture @ University of Rochester regarding "Heterogeneous Machine Learning and its applications".
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Oct, 2024 |
One paper "DrGNN: Deep Residual Graph Neural Network with Contrastive Learning" accepted @ TMLR'24.
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Sep, 2024 |
One paper "Multi-label Sequential Sentence Classification via Large Language Model" accepted @ EMNLP’24.
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May, 2024 |
One paper Heterogeneous Contrastive Learning for Foundation Models and Beyond accepted @ KDD’24.
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Jan, 2024 |
One paper MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems accepted @ WWW’24.
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Nov, 2023 |
One paper FairGen: Towards Fair Graph Generation accepted @ ICDE’24.
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Dec, 2023 |
One paper Fairness-aware multi-view clustering accepted @ SDM’23.
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Sep, 2022 |
One paper MentorGNN: Deriving Curriculum for Pre-Training GNNs accepted @ CIKM’22.
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May, 2022 |
One paper Contrastive learning with complex heterogeneity accepted @ KDD’22.
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Dec, 2021 |
One paper Outlier impact characterization for time series data accepted @ AAAI’22.
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Jan, 2021 |
One paper Deep co-attention network for multi-view subspace learning accepted @ WWW’21.
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Publications (*equal contribution)
       Conference
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Multi-label Sequential Sentence Classification via Large Language Model
Mengfei Lan, Lecheng Zheng, Shufan Ming, Halil Kilicoglu.
EMNLP 2024
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Heterogeneous Contrastive Learning for Foundation Models and Beyond
Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, Jingrui He
KDD 2024
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MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems
Lecheng Zheng, Zhengzhang Chen, Jingrui He, Haifeng Chen
Web Conference 2024 (Oral Presentation) Code
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FairGen: Towards Fair Graph Generation
Lecheng Zheng, Dawei Zhou, Hanghang Tong, Jiejun Xu, Yada Zhu, Jingrui He
ICDE 2024 Code
- Fairness-aware multi-view clustering
Lecheng Zheng, Yada Zhu, Jingrui He
SDM 2023 Code
- MentorGNN: Deriving Curriculum for Pre-Training GNNs
Dawei Zhou*, Lecheng Zheng*, Dongqi Fu, Jiawei Han, Jingrui He
CIKM 2022 Code
- Contrastive learning with complex heterogeneity
Lecheng Zheng*, Jinjun Xiong, Yada Zhu, Jingrui He
KDD 2022 Code
- Outlier impact characterization for time series data
Jianbo Li, Lecheng Zheng, Yada Zhu, Jingrui He
AAAI 2022. Code
- Deep co-attention network for multi-view subspace learning
Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He
Web Conference 2021 Code
- A data-driven graph generative model for temporal interaction networks
Dawei Zhou, Lecheng Zheng, Jiawei Han, Jingrui He
KDD 2020 Code
- Domain adaptive multi-modality neural attention network for financial forecasting
Dawei Zhou, Lecheng Zheng, Yada Zhu, Jianbo Li, Jingrui He
Web Conference 2020
- Deep multimodality model for multi-task multi-view learning
Lecheng Zheng, Yu Cheng, Jingrui He
SDM 2019. Code
              Journal
              Preprint
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Online Multi-modal Root Cause Analysis
Lecheng Zheng, Zhengzhang Chen, Haifeng Chen, Jingrui He
preprint 2024.
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Fair Anomaly Detection For Imbalanced Groups
Ziwei Wu*, Lecheng Zheng*, Yuancheng Yu, Ruizhong Qiu, John Birge, Jingrui He
preprint 2024.
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Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering
Lecheng Zheng, John R Birge, Yifang Zhang, Jingrui He
preprint 2024.
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LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis
Lecheng Zheng*, Zhengzhang Chen*, Dongjie Wang*, Chengyuan Deng*, Reon Matsuoka, Haifeng Chen
preprint 2024. CodeDataset
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
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