Wei Cheng Senior Research Staff Member Data Science Department NEC Laboratories America NC 27599-3175, USA Email: weicheng [AT] nec-labs [DOT] com  Homepage: https://chengw07.github.io

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About Me

Wei Cheng is a Senior Researcher at NEC Labs America. He received his Ph.D. from the Department of Computer Science, UNC at Chapel Hill in 2015, advised by Prof. Wei Wang. His research interests include data science, machine learning and bioinformatics. He has filed more than eighty patents, and has published more than 100 research papers in top-tier conferences such as NeurIPS, ICML, SIGKDD, ICLR, WWW, EMNLP, ISMB and journals such as Nature, Science, TPAMI, TNNLS, TKDE, Bioinformatics, etc. His research results received Best Research Paper Runner-Up Award at SIGKDD 2016 and were nominated for the Best Paper Award at ICDM 2018, ICDM 2017, ICDM 2015 and SDM 2012. He has also served as area chairs for several top-tier conferences including SIGKDD, EMNLP, NAACL, IJCAI, SDM, AAAI, WSDM, etc.

Email | Google Scholar | LinkedIn | GitHub

Research Interests

• Data Science: Time Series Analysis, Anomaly Detection, Recommendation
• Machine Learning: Deep Learning, GNN, Reinforcement Learning, Transfer Learning, Meta-Learning, Federated Learning, Imitation Learning
• NLP: LLM-generated Content Detection, LLM Adaptation, Text Representation Learning, Sentiment Analysis
• Bioinformatics: eQTL Study, Gene Pathway Analysis

Education

• Ph.D., Department of Computer Science, University of North Carolina at Chapel Hill, 2015. Advisor: Prof. Wei Wang (UCLA).
• M.E., Tsinghua University, 2010.
• B.E., Nanjing University, 2006.

Selected Publications

Preprint

• PAC Learnability under Explanation-Preserving Graph Perturbations.
  Xu Zheng, Farhad Shirani, Tianchun Wang, Shouwei Gao, Wenqian Dong, Wei Cheng, Dongsheng Luo.
  (PDF)

• Exploring the limits of chatgpt for query or aspect-based text summarization.
  Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, *Wei Cheng.
  . (PDF)

• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey.
  C. Ling, X. Zhao, J. Lu, C. Deng, C. Zheng, J. Wang, T. Chowdhury, Y. Li, H. Cui, X. Zhang, T. Zhao, A. Panalkar, Wei Cheng, H. Wang, Y. Liu, Z. Chen, H. Chen, C. White, Q. Gu, C. Yang, L. Zhao.
  . (PDF)

2025

• DISC: Dynamic Decomposition Improves LLM Inference Scaling.
  Jonathan Light, *Wei Cheng, Yue Wu, Masafumi Oyamada, Mengdi Wang, Santiago Paternain, Haifeng Chen
  In ICLR Workshop on Deep Learning for Code, and ICLR Workshop on Scaling Self-Improving Foundation Models without Human Supervision (SSI-FM).(PDF)

• Where's the liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content.
  Haoyue Bai, Yiyou Sun, Wei Cheng, Haifeng Chen.
  In The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’25).(PDF)

• Position Really Matters: Towards a Holistic Approach for Prompt Tuning.
  Xianjun Yang, *Wei Cheng, Xujiang Zhao, Wenchao Yu, Linda Petzold, Haifeng Chen.
  In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’25).(PDF)

• MixLLM: Dynamic Routing in Mixed Large Language Models.
  Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen.
  In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’25).(PDF)

• Chain-of-region: Visual Language Models Need Details for Diagram Analysis.
  Xue Li, Yiyou Sun, Wei Cheng, Yinglun Zhu, Haifeng Chen.
  In The Thirteenth International Conference on Learning Representations (ICLR'25)), 2025.(PDF)

• F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI,
  Xu Zheng, Farhad Shirani, Zhuomin Chen, Chaohao Lin, Wei Cheng, Wenbo Guo, Dongsheng Luo.
  In The Thirteenth International Conference on Learning Representations (ICLR'25)), 2025.(PDF)

• Scattered Forest Search: Smarter Code Space Exploration with LLMs Scaling Inference.
  Jonathan Light, Yue Wu, Yiyou Sun, Wenchao Yu, Yanchi Liu, Xujiang Zhao, Ziniu Hu, Haifeng Chen, *Wei Cheng.
  In The Thirteenth International Conference on Learning Representations (ICLR'25)), 2025.(PDF)

• Strategist: Self-improvement of LLM Decision Making via Bi-Level Tree Search.
  Jonathan Light, Min Cai, Weiqin Chen, Guanzhi Wang, Xiusi Chen, Wei Cheng, Yisong Yue, Ziniu Hu.
  In The Thirteenth International Conference on Learning Representations (ICLR'25)), 2025.(PDF)

• Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors.
  Tianchun Wang, Yuanzhou Chen, Zichuan Liu, Zhanwen Chen, Haifeng Chen, Xiang Zhang, Wei Cheng.
  In The Thirteenth International Conference on Learning Representations (ICLR'25)), 2025.(PDF)

• TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents..
  Geon Lee, Wenchao Yu, Kijung Shin, Wei Cheng, Haifeng Chen.
  In The The 2025 AAAI International Conference on Artificial Intelligence (AAAI'25), 2025.(PDF)

2024

• Protecting Your LLMs with Information Bottleneck.
  Zichuan Liu, Zefan Wang, Linjie Xu, Jinyu Wang, Lei Song, Tianchun Wang, Chunlin Chen, Wei Cheng, Jiang Bian.
  In Conference on Neural Information Processing Systems (NeurIPS'24), 2024.(PDF)

• Improving Logits-based Detector without Logits from Black-box LLMs.
  Cong Zeng, Shengkun Tang, Xianjun Yang, Yuanzhou Chen, Yiyou Sun, Zhiqiang xu, Yao Li, Haifeng Chen, *Wei Cheng, Dongkuan Xu.
  In Conference on Neural Information Processing Systems (NeurIPS'24), 2024.(PDF)

• TrustAgent: Towards Safe and Trustworthy LLM-based Agents through Agent Constitution.
  Wenyue Hua, Xianjun Yang, Zelong Li, Wei Cheng, Ruixiang Tang, Yongfeng Zhang.
  In Empirical Methods in Natural Language Processing (EMNLP'24), 2024.(PDF)

• Large Language Models Can Be Contextual Privacy Protection Learners.
  Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, *Wei Cheng
  In Empirical Methods in Natural Language Processing (EMNLP'24), 2024.(PDF)

• A Survey on Detection of LLMs-Generated Content.
  Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, *Wei Cheng
  In Empirical Methods in Natural Language Processing (EMNLP'24), 2024.(PDF)

• InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration.
  Fali Wang, Runxue Bao, Suhang Wang, Wenchao Yu, Yanchi Liu, Wei Cheng, Haifeng Chen.
  In Empirical Methods in Natural Language Processing (EMNLP'24), 2024. (PDF)

• Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search.
  Jonathan Light, Min Cai, Weiqin Chen, Guanzhi Wang, Xiusi Chen, Wei Cheng, Yisong Yue, Ziniu Hu.
  In ICML Workshop: AutoRL, 2024.(PDF)

• TrustAgent: Towards Safe and Trustworthy LLM-based Agents through Agent Constitution.
  Wenyue Hua, Xianjun Yang, Zelong Li, Wei Cheng, Ruixiang Tang, Yongfeng Zhang.
  In ICML Workshop: TiFA, 2024.(PDF)

• DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton.
  Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, Haifeng Chen.
  International Conference on Machine Learning (ICML'24), 2024. (PDF)

• POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning.
  Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, Haifeng Chen
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24), 2024.(PDF)

• Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization.
  Haohui Wang, Baoyu Jing, Kaize Ding, Yada Zhu, Wei Cheng, Si Zhang, Yonghui Fan, Liqing Zhang, Dawei Zhou.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24), 2024. (PDF)

• Uncertainty Decomposition and Quantification for In-Context Learning of Large Language Models.
  Chen Ling, Xujiang Zhao, Wei Cheng, Yanchi Liu, Yiyou Sun, Xuchao Zhang, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen.
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL'24), 2024.(PDF)

• Generality and Specificity: Pruning as a Domain-specific LLM Extractor.
  Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen.
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL'24), 2024.(PDF)

• Towards Inductive and Efficient Explanations for Graph Neural Networks.
  Dongsheng Luo, Tianxiang Zhao, Wei Cheng, Dongkuan Xu, Feng Han, Wenchao Yu, Xiao Liu, Haifeng Chen, Xiang Zhang.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024.(PDF)

• Parametric Augmentation for Time Series Contrastive Learning.
  Xu Zheng, Tianchun Wang, Wei Cheng, Aitian Ma, Haifeng Chen, Mo Sha, Dongsheng Luo.
  International Conference on Learning Representations (ICLR'24).(PDF)

• Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks.
  Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei, Dongsheng Luo.
  International Conference on Learning Representations (ICLR'24).(PDF)

• DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text.
  Xianjun Yang, *Wei Cheng, Linda Petzold, William Yang Wang, Haifeng Chen.
  International Conference on Learning Representations (ICLR'24).(PDF)

• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty.
  Chen Ling, Xujiang Zhao, Xuchao Zhang, Yanchi Liu, Wei Cheng, Haoyu Wang, Zhengzhang Chen, Mika Oishi, Takao Osaki, Katsushi Matsuda, Liang Zhao, Haifeng Chen.
  In ICLR Workshop.(PDF)

• Towards Robust Fidelity for Evaluating Explainability.
  Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei and Dongsheng Luo.
  In The Second Workshop on Trustworthy Learning on Graphs with WWW.(PDF)

• DyExplainer: Explainable Dynamic Graph Neural Networks.
  Tianchun Wang, Dongsheng Luo, Wei Cheng, Haifeng Chen, Xiang Zhang.
  The 17th ACM International WSDM Conference, 2024. (Paper)

• Interpretable Imitation Learning with Dynamic Causal Relations.
  Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen.
  The 17th ACM International WSDM Conference, 2024. (PDF)

2023

• Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models.
  Jianwei Li, Qi Lei, Wei Cheng, Dongkuan Xu.
  Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023. (PDF)

• Large Language Models Can Be Good Privacy Protection Learners.
  Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, *Wei Cheng
  In Southern California NLP Symposium, 2023.(PDF)

• Zero-Shot Detection of Machine-Generated Codes.
  Xianjun Yang, Kexun Zhang, Haifeng Chen, Linda Petzold, William Yang Wang, *Wei Cheng.
  In Southern California NLP Symposium, 2023. (PDF)

• Open-ended Commonsense Reasoning with Unrestricted Answer Candidates.
  Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao.
  Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023 . (PDF) (Findings)

• Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning.
  Yizhou Zhang, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Liang Tong, Haifeng Chen, Yan Liu.
  Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2023. (PDF)

• Personalized Federated Learning under Mixture of Distributions.
  Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, *Wei Cheng.
  International Conference on Machine Learning (ICML'23), 2023. (PDF)

• FedSkill: Privacy Preserved Interpretable Skill Learning via Imitation.
  Yushan Jiang, Wenchao Yu, Dongjin Song, Lu Wang, Wei Cheng, Haifeng Chen.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023. (To Appear)

• Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations.
  Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023. (To Appear)

• AutoTCL: Automated Time Series Contrastive Learning with Adaptive Augmentations.
  Xu Zheng, Tianchun Wang, Wei Cheng, Aitian Ma, Haifeng Chen, Mo Sha, Dongsheng Luo.
  The Second Workshop of Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications, 2023. (To Appear) (Best Paper Award)

• Unsupervised anomaly detection under a multiple modeling strategy via model set optimization through transfer learning.
  Masanao Natsumeda, Takehiko Mizoguchi, Wei Cheng, Yuncong Chen and Haifeng Chen.
  26th International Conference on Information Fusion, 2023.

• Knowledge-enhanced Prompt for Open-domain Commonsense Reasoning.
  Chen Ling, Xuchao Zhang, Xujiang Zhao, Yifeng Wu, Yanchi Liu, Wei Cheng, Haifeng Chen, Zhao Liang.
  In AAAI Workshop on Uncertainty Reasoning and Quantification in Decision Making.

• Interpretable Skill Learning for Dynamic Treatment Regimes Through Imitation.
  Y Jiang, W Yu, D Song, Wei Cheng, H Chen.
  Invited paper at 57th Annual Conference on Information Science and Systems (CISS'23).. (Link)

• Time Series Contrastive Learning with Information-Aware Augmentations.
  Dongsheng Luo, *Wei Cheng, Y. Wang, Dongkuan Xu, Wenchao Yu, Xuchao Zhang, Jingchao Ni, Yanchi Liu, Yuncong Chen, Haifeng Chen, Xiang Zhang.
  In The 2023 AAAI International Conference on Artificial Intelligence (AAAI'23). (PDF)

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). (PDF)

• Deep Federated Anomaly Detectiion for Multivariate Time Series Data.
  Wei zhu, Dongjin Song, Yuncong Chen, Wei Cheng, Bo Zong, Takehiko Mizoguchi, Cristian Lumezanu, Haifeng Chen, Jiebo Luo.
  In 2022 IEEE International Conference on Big Data (IEEE Big Data).

• CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences.
  Shengming Zhang, Yanchi Liu, Xuchao Zhang, Wei Cheng, Haifeng Chen, Hui Xiong.
  In 2022 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'22).

• Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings.
  Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, Haifeng Chen.
  In International Conference on Learning Representations (ICLR'22).(PDF)(Spotlight)

• Code Editing from Few Exemplars by Adaptive Multi-Extent Composition.
  Peizhao Li, Xuchao Zhang, Ziyu Yao, Wei Cheng, Haifeng Chen, Hongfu Liu.
  ICLR Workshop of Deep Learning For Code (ICLR Workshop), 2022.(PDF)

• SEED: Sound Event Early Detection via Evidential Uncertainty.
  Xujiang Zhao, Xuchao Zhang, Wei Cheng, Wenchao Yu, Yuncong Chen, Haifeng Chen, Feng Chen.
  In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'22).(To Appear)

• Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph.
  Liyan Xu, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Zhao Liang, Jinho D. Choi.
  In The 2022 AAAI International Conference on Artificial Intelligence (AAAI'22).(to appear)

2021

• InfoGCL: Information-Aware Graph Contrastive Learning.
  Dongkuan Xu, *Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang.
  In Conference on Neural Information Processing Systems, (NeurIPS’21).(PDF,Supp)

• Dynamic Causal Discovery in Imitation Learning.
  Tianxiang Zhao, Wenchao Yu, Lu Wang, Shuhang Wang, Wei Cheng, Xiang Zhang, Yuncong Chen, Xuchao Zhang, Haifeng Chen.
  In Sequential Decision Making: Bridging Theory and Practice (NeurIPS Workshop).(to appear)

• Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction.
  Zeyu Li, *Wei Cheng, Reema Kshetramade, John Houser, Haifeng Chen, Wei Wang.
  In The Conference on Empirical Methods in Natural Language Processing(EMNLP'21).(PDF, Code)

• Aspect-based Sentiment Classification via Reinforcement Learning.
  Lichen Wang, Bo Zong, Yunyu Liu, Can Qin, Wei Cheng, Wenchao Yu, Xuchao Zhang, Haifeng Chen, Yun Fu.
  In The 2021 edition of the IEEE International Conference on Data Mining series (ICDM'21).(PDF)

• You Are What and Where You Are: Graph Enhanced Attention Network for Explainable POI Recommendation.
  Zeyu Li, *Wei Cheng, Haiqi Xiao, Wenchao Yu, Haifeng Chen, Wei Wang.
  In The Conference on Information and Knowledge Management (CIKM'21), 2021.(PDF, Code)

• Interpreting Convolutional Sequence Model by Learning Local Prototypes with Adaptation Regularization.
  Jingchao Ni, Zhengzhang Chen, Wei Cheng, Bo Zong, Dongjin Song, Yanchi Liu, Xuchao Zhang, Haifeng Chen.
  In The Conference on Information and Knowledge Management (CIKM'21), 2021.(PDF)

• Hierarchical Imitation Learning with Contextual Bandits for Dynamic Treatment Regimes.
  Lu Wang, Wenchao Yu, Wei Cheng, Bo Zong and Haifeng Chen.
  In ICML Workshop of Reinforcement Learning for Real Life (ICML Workshop), 2021.(PDF)

• Unsupervised Concept Representation Learning for Length-Varying Text Similarity.
  Xuchao Zhang, Bo Zong, Wei Cheng, Jingchao Ni, Yanchi Liu and Haifeng Chen.
  In Proceedings of The 2021 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL-HLT'21), 2021.(PDF)

• FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems.
  Liang Tong, Zhengzhang Chen, Jingchao Ni, Wei Cheng, Dongjing Song, Yevgeniy Vorobeychik.
  In Proceedings of the 2021 Conference on Computer Vision and Pattern Recognition (CVPR'21), 2021.(PDF)

• Deep Multi-Instance Contrastive Learning with Dual Attention for Anomaly Precursor Detection.
  Dongkuan Xu, *Wei Cheng, Jingchao Ni, Dongsheng Luo, Masanao Natsumeda, Dongjing Song, Bo Zong, Haifeng Chen, Xiang Zhang.
  In Proceedings of the 21th SIAM International Conference on Data Mining (SDM'21)., 2021.(PDF)

• Multi-Task Recurrent Modular Networks.
  Dongkuan Xu, *Wei Cheng, Bo Zong, Wenchao Yu, Jingchao Ni, Dongjing Song, Xuchao Zhang, Haifeng Chen, Xiang Zhang.
  The 2021 AAAI International Conference on Artificial Intelligence (AAAI’21), 2021.(PDF)(Similar Structure as Google PathWays)

• Transformer-Style Relational Reasoning with Dynamic Memory Updating for Temporal Network Modeling.
  Dongkuan Xu, Junjie Liang, Wei Cheng, Hua Wei, Haifeng Chen, Xiang Zhang.
  The 2021 AAAI International Conference on Artificial Intelligence (AAAI’21), 2021.(PDF)(Code)

• Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series.
  Yinjun Wu, Jingchao Ni, Wei Cheng, Bo Zong, Zhengzhang Chen, Dongjing Song, Haifeng Chen.
  The 2021 AAAI International Con-ference on Artificial Intelligence (AAAI’21), 2021.(PDF)(Code)

• Learning to Drop: Robust Graph Neural Network via Topological Denoising.
  Dongsheng Luo, *Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, Xiang Zhang
  The 14th ACM International WSDM Conference (WSDM'21), 2021.(PDF)(Code)

• Mapping Genomic Loci Prioritises Genes and Implicates Synaptic Biology in Schizophrenia.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ)
  Nature, 2021.

2020

• Parameterized Explainer for Graph Neural Network.
  Dongsheng Luo, *Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang
  Conference on Neural Information Processing Systems (NeurIPS'20), 2020.(PDF, Slides, Poster, Code&Data)(Slides)(ML Reproducibility Challenge)

• Robust Graph Representation Learning via Neural Sparsification.
  Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang
  International Conference on Machine Learning (ICML'20), 2020.(PDF, Supp)(Code)

• Node Classification in Temporal Graphs through Stochastic Sparsification and Temporal Structural Convolution.
  Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'20), 2020.(PDF)

• T2-Net: A Semi-supervised Deep Model for Turbulence Forecasting.
  Denghui Zhang, Yanchi Liu, Wei Cheng, Bo Zong, Jingchao Ni, Zhengzhang Chen, Haifeng Chen, Hui Xiong
  The 2020 edition of the IEEE International Conference on Data Mining series (ICDM'20)(PDF)

• You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis.
  Q. Wang, W. Hassan, D. Li, K. Jee, X. Yu, K. Zou, J. Rhee, Z. Chen, W. Cheng, C. Gunter, H. Chen
  The Network and Distributed System Security Symposium (NDSS'20), 2020. (PDF)

• Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes.
  Lu Wang, Wenchao Yu, Wei Cheng, Martin Renqiang Ren, Bo Zong, Xiaofeng He, Hongyuan Zha, Wei Wang, Haifeng Chen
  Proceedings of the International Conference on World Wide Web (WWW'20), 2020. (PDF)

• Inductive and Unsupervised Representation Learning on Graph Structured Objects.
  Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni, Wenchao Yu, Yanchi Liu, Dongjin Song, Haifeng Chen, Yun Fu
  The Eighth International Conference on Learning Representations (ICLR'20), 2020. (PDF)

• Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series.
  D. Xu, *Wei Cheng, B. Zong, D. Song, J. Ni, W. Yu, Y. Liu, H. Chen, X. Zhang
  The 2020 AAAI International Conference on Artificial Intelligence (AAAI'20), 2020.(to appear) (PDF) (Code)

• Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-based Recommendation.
  X. Dong, J. Ni, Wei Cheng, Z. Chen, B. Zong, D. Song, Y. Liu, H. Chen, G. Melo
  The 2020 AAAI International Conference on Artificial Intelligence (AAAI'20), 2020.(to appear) (PDF)

• Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval.
  D. Zhu, D. Song, Y. Chen, C. Lumezanu, Wei Cheng, B. Zong, J. Ni, T. Mizoguchi, T. Yang, H. Chen
  The 2020 AAAI International Conference on Artificial Intelligence (AAAI'20), 2020.(to appear) (PDF)

• Temporal Context-Aware Representation Learning for Question Routing. 
  Xuchao Zhang, Wei Cheng, Bo Zong, Yuncong Chen, Jianwu Xu, Ding Li, Haifeng Chen.
  The 13th ACM International WSDM Conference (WSDM'20), 2020. (PDF)
  
  
• Interpretable Click-Through Rate Prediction through Hierarchical Attention. 
  Zeyu Li, *Wei Cheng, Yang Chen, Haifeng Chen and Wei Wang.
  The 13th ACM International WSDM Conference (WSDM'20) , 2020. (Oral Presentation,PDF)(Code)
  
  
2019
• Learning Robust Representations with Graph Denoising Policy Network. 
  Lu Wang, Wenchao Yu, Wei Wang, Wei Cheng, Hongyuan Zha, Wei Zhang, Xiaofeng He, and Haifeng Chen.
  The 2019 edition of the IEEE International Conference on Data Mining series (ICDM'19), 2019.(PDF)
  
  
• 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, and Xiang Zhang.
  The 2019 edition of the IEEE International Conference on Data Mining series (ICDM'19), 2019.(PDF)
  
  
• Self-Attentive Attributed Network Embedding Through Adversarial Learning. 
  Wenchao Yu, *Wei Cheng, Charu Aggarwal, Bo Zong, Haifeng Chen, and Wei Wang.
  The 2019 edition of the IEEE International Conference on Data Mining series (ICDM'19), 2019.(PDF)
  
  
• Memory-Based Random Walk for Multi-Query Local Community Detection. 
  Yuchen Bian, Dongsheng Luo, Yaowei Yan, Wei Cheng, Wei Wang, Xiang Zhang.
  Knowledge and Information Systems (KAIS), 2019. (Best Papers of ICDM 2018)(PDF)
  
  

• Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mapping.
  Wei Cheng, Xiang Zhang, and Wei Wang
  In "eQTL Analysis: Methods and Protocols"(Book Chapter, Eds: Xinghua Mindy Shi), Chapter 8, Springer Press. 2019 (PDF)

• Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs.
  Dongkuan Xu, *Wei Cheng, Dongsheng Luo, Xiao Liu, Xiang Zhang
  In Proceedings of The 29th International Joint Conference on Artificial Intelligence (IJCAI'19). (PDF) (Code)

• Deep Co-Clustering.
  Dongkuan Xu, *Wei Cheng, Bo Zong, Jingchao Ni, Dongjin Song, Wenchao Yu, Yuncong Chen, Haifeng Chen, Xiang Zhang
  In Proceedings of the 19th SIAM International Conference on Data Mining (SDM'19). (PDF, Supp,Code)

• A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data.
  C. Zhang, D. Song, Y. Chen, X. Feng, C. Lumezanu, W. Cheng, J. Ni, B. Zong, H. Chen, N. Chawla
  The 2019 AAAI International Conference on Artificial Intelligence (AAAI'19), 2019.(PDF)

• Population‐based identity‐by‐descent mapping combined with exome sequencing to detect rare risk variants for schizophrenia..
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ)
  The American Journal of Human Genetics, , 2019.

2018

• Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  Cell, 2018.

• On Multi-Query Local Community Detection. 
  Yuchen Bian, Yaowei Yan, Wei Cheng, Wei Wang, Dongsheng Luo, Xiang Zhang.
  The 2018 edition of the IEEE International Conference on Data Mining series (ICDM'18), 2018. (Knowl. Inf. Syst. (KAIS) on “Bests of ICDM 2018”)
  
  

• Collaborative Alert Ranking for Enterprise Security System.
  Ying Lin, Zhengzhang Chen, Kai Zhang, Cheng Cao, Lu-An Tang, Wei Cheng and Zhichun Li.
  The 27th ACM International Conference on Information and Knowledge Management (CIKM'18), 2018. (pdf)

• Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  The American Journal of Human Genetics, 2018.

• NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks.
  Wenchao Yu, *Wei Cheng, Charu Aggarwal, Kai Zhang, Haifeng Chen, Wei Wang.
  The Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’18), 2018. (Oral, pdf)(python code)

• Deep r-th Root Rank Supervised Joint Binary Embedding for Multivariate Time Series Retrieval.
  Dongjing Song, Ning Xia, Wei Cheng, Haifeng Chen, Dacheng Tao.
  The Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’18), 2018.(Oral,pdf, Media Report)

• Learning Deep Network Representations with Adversarially Regularized Autoencoders.
  Wenchao Yu, Cheng Zheng, *Wei Cheng, Charu Aggarwal, Dongjing Song, Bo Zong, Haifeng Chen, Wei Wang.
  The Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’18), 2018. (Oral, pdf, python code)

• DBSDA: Lowering the Error Bound of Sparse Linear Discriminant Analysis via Model De-Biasing.
  Haoyi Xiong, Wei Cheng, Wenqing Hu, Jiang Bian, Zhishan Guo.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018.
  
  
• Analysis of Shared Heritability in Common Disorders of the Brain.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  Science , 2018.
  
  
• Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection.
  Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen.
  Sixth International Conference on Learning Representations (ICLR'18), 2018.
  
  
• Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach.
  Liang Lan, Zhuang Wang, Wei Cheng, Kai Zhang.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018.
  
  
• Co-Regularized Deep Multi-Network Embedding.
  Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu and Xiang Zhang.
  Proceedings of the International Conference on World Wide Web (WWW'18), 2018.
  
  
• The Multi-Walker Chain and Its Application in Local Community Detection. 
  Yuchen Bian, Jingchao Ni, Wei Cheng, Xiang Zhang.
  Knowledge and Information Systems (KAIS), 2018. (Best Papers of ICDM 2017)
  
  
• De-biasing Covariance-Regularized Discriminant Analysis.
  Haoyi Xiong, Wei Cheng, Yanjie Fu, Jiang Bian, Wenqing Hu and Zhishan Guo.
  The 28th International Joint Conference on Artificial Intelligence (IJCAI'18), 2018. (PDF)
  
  
2017
• Identifying and Quantifying Nonlinear Structured Relationships in Complex Manufactural Systems.
  T. Xu, T. Yan, D. Song, Wei Cheng, H. Chen, G. Jiang and J. Bi
  IEEE International Conference on Big Data (Big Data 2017), Boston, MA, December, 2017.
  
  
• ComClus: A Self-Grouping Framework for Multi-Network Clustering. 
  Jingchao Ni, Wei Cheng, Wei Fan, Xiang Zhang.
  Transactions on Knowledge and Data Engineering (TKDE), 2017.
  
  
• Link Prediction with Spatial and Temporal Consistency in Dynamic Networks. 
  Wenchao Yu, *Wei Cheng, Wei Wang, Charu C Aggarwal, Haifeng Chen. 
  Proc. 27th Intl. Joint Conf. on Artificial Intelligence (IJCAI'17), 2017. (PDF)
  
  
• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. 
  Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Geoff Jiang, Garrison Cottrell. 
  Proc. 27th Intl. Joint Conf. on Artificial Intelligence (IJCAI'17), 2017. (PDF).(Most influential papers on IJCAI'17 by paperdigest.org)
  
  
• Many Heads are Better than One: Local Community Detection by the Multi-Walker Chain. 
  Yuchen Bian, Jingchao Ni, Wei Cheng, Xiang Zhang.
  The 2017 edition of the IEEE International Conference on Data Mining series (ICDM'17), 2017. (Knowl. Inf. Syst. (KAIS) on “Bests of ICDM 2017”)
  
  
• Ranking Causal Anomalies by Modeling Local Propagations on Networked Systems.
  Jingchao Ni, *Wei Cheng, Kai Zhang, Dongjin Song, Tan Yan, Haifeng Chen, Xiang Zhang.
  The 2017 edition of the IEEE International Conference on Data Mining series (ICDM'17), 2017.
  
  
• Multi-Party Sparse Discriminant Learning. 
  Jian Bian, Haoyi Xiong, Wei Cheng, Yanjie Fu, Wenqing Hu, Zhishan Guo. 
  The 2017 edition of the IEEE International Conference on Data Mining series (ICDM'17), 2017.
  
  
• AWDA: An Adaptive Wishart Discriminant Analysis. 
  Haoyi Xiong, Wei Cheng, Wenqing Hu, Jiang Bian, and Zhishan Guo.
  The 2017 edition of the IEEE International Conference on Data Mining series (ICDM'17), 2017.
  
  
• Low-rank Decomposition Meets Kernel Learning: A Generalized Nystrom Method . 
  Liang Lan, Kai Zhang, Hancheng Ge, Wei Cheng, Jun Liu, Andreas Rauber, Xiao-Li Li, Jun Wang, Hongyuan Zha. 
  Artificial Intelligence, 2017.
  
  
• Genetic correlation between amyotrophic lateral sclerosis and schizophrenia.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  Nature Communications, 2017.
  
  
• Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  Nature Genetics, 2017.(paper)
  
  
• Ranking Causal Anomalies for System Fault Diagnosis via Temporal and Dynamical Analysis on Vanishing Correlations. 
  Wei Cheng, Jingcao Ni, Kai Zhang, Haifeng Chen, Guofei Jiang, Yu Shi, Xiang Zhang, Wei Wang. 
  Transactions on Knowledge Discovery from Data (TKDD), 2017. (Paper)(Best Papers of KDD 2016)
  
  
2016
• Self-Grouping Multi-Network Clustering. 
  Jingcao Ni, Wei Cheng, Wei Fan, Xiang Zhang. 
  In Proceedings of the IEEE International Conference on Data Mining series (ICDM'16), 2016. (Paper)(Appendix)
  
  
• Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations.
  Wei Cheng, Kai Zhang, Haifeng Chen, Guofei Jiang, Zhengzhang Chen, Wei Wang.
  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(SIGKDD'16), 2016. (Award)(paper)(code)(Best Paper Runner Up Award)(Award Plaque)
  
  
• Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof-of-concept and roadmap for future studies.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  Nature Neurosci, 2016.(paper)
  
  
• Robust Framework on Multi-Network Clustering via Joint Cross-Domain Cluster Alignment.
  Rui Liu, Wei Cheng, Hanghang Tong, Wei Wang, Xiang Zhang.
  Knowledge and Information Systems (KAIS), 2016. (Joint First Author,Best Papers of ICDM 2018)
  
  
• Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mapping. 
  Wei Cheng, Yu Shi, Xiang Zhang, Wei Wang.
  BMC Bioinformatics, 2016. (PDF)(Appendix)
  
  
• CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering. 
  Wei Cheng, Zhishan Guo, Xiang Zhang, Wei Wang. 
  Transactions on Knowledge Discovery from Data (TKDD), 2016. (PDF)
  
  
• Robust Methods for Expression Quantitative Trait Loci Mapping. 
  Wei Cheng, Xiang Zhang, Wei Wang.
  Big Data Analytics in Genomics. Springer (New York), 2016. (PDF)
  
  
2015 and Before
• Toward Robust Group-Wise eQTL Mapping via Integrating Multi-Domain Heterogeneous Data. 
  Wei Cheng.
  Ph.D. Thesis, University of North Carolina at Chapel Hill, 2015. (PDF)
  
  
• Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment.
  Rui Liu, *Wei Cheng, Hanghang Tong, Wei Wang, Xiang Zhang. 
  Proceedings of the IEEE International Conference on Data Mining (ICDM'15), 2015. (paper) (*Joint First Author, Knowl. Inf. Syst. (KAIS) on “Bests of ICDM 2015”)
  
  
• Modeling linkage disequilibrium increases accuracy of polygenic risk scores.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  The American Journal of Human Genetics, 2015.
  
  
• HICC: An Entropy Splitting Based Framework for Hierarchical Co-Clustering.
  Wei Cheng, Xiang Zhang, Feng Pan, Wei Wang. 
  Knowledge and Information Systems(KAIS), 2015. (paper)(code)
  
  
• Fast and Robust Group-Wise eQTL Mapping Using Sparse Graphical Models.
  Wei Cheng, Yu Shi, Xiang Zhang, Wei Wang.
  BMC Bioinformatics, 2015.(paper)
  
  
• Biological Insights From 108 Schizophrenia-Associated Genetic Loci.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  Nature, 2014. (paper).
  
  
• Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases.
  Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC-SCZ).
  The American Journal of Human Genetics, 2014.
  
  
• Graph Regularized Dual Lasso for Robust eQTL Mapping. 
  Wei Cheng, Xiang Zhang, Zhishan Guo, Yu Shi and Wei Wang.
  In Proceedings of the 22nd Annual International Conference on Intelligent Systems for Molecular Biology (ISMB'14) , 2014, (paper)(slides)(code).
  
  
• Flexible and Robust Co-Regularized Multi-Domain Graph Clustering.
  Wei Cheng, Xiang Zhang, Zhishan Guo, Yubao Wu, Patrick Sullivan and Wei Wang.
  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'13), Chicago, 2013.(PDF) (matlab code&data)(C++ code).
  
  
• Searching Dimension Incomplete Databases.
  Wei Cheng, Xiaoming Jin, Jiantao Sun, Xuemin Lin, Xiang Zhang, Wei Wang.
  Transactions on Knowledge and Data Engineering (TKDE), 2013. (PDF)
  
  
• Grid-based Clustering.
  Wei Cheng, Wei Wang, Sandra Batista. 
  In "Data Clustering: Algorithms and Applications"(Eds: Charu C. Aggarwal, Chandan K. Reddy), Chapter 6, CRC Press, 2012. (PDF)
  
  
• Inferring novel associations between SNP sets and gene sets in eQTL study using sparse graphical model.
  Wei Cheng, Xiang Zhang, Yubao Wu, Xiaolin Yin, Jing Li, David Heckerman and Wei Wang. 
  In Proceedings of the third ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB'12), Orlando, Florida 2012. (PDF)
   
• Hierarchical Co-Clustering Based on Entropy Splitting.
  Wei Cheng, Xiang Zhang, Feng Pan, Wei Wang.
  In Proceedings of the 21st ACM Conference on Information and Knowledge Management (ACM CIKM'12), Maui, HI 2012. (PDF)
  
  
• Learning transcriptional regulatory relationship using sparse graphical models.
  Xiang Zhang, Wei Cheng, Jennifer Listgarten, Carl Kadie, Shunping Huang, Wei Wang, David Heckerman.
  PLoS One , 2012. (PDF) (Joint First Author)
  
  
• Dual Transfer Learning.
  Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, Wei Wang . 
  In Proceedings of the 12th SIAM International Conference on Data Mining (SIAM SDM'12). Anaheim, CA USA, April 2012. (PDF) (Statistical Analysis and Data Mining on “Bests of SDM 2012”)
  
  
• Measuring Opinion Relevance in Latent Topic Space.
  Wei Cheng, Xiaochuan Ni, Jian-Tao Sun, Xiaoming Jin, Hye-chung Kum, Xiang Zhang, Wei Wang. 
  In Proceedings of the Third IEEE International Conference on Social Computing (IEEE SocialCom 2011) MIT, MA USA, 2011. (PDF) 
  
  
• Transfer Learning via Cluster Correspondence Inference.
  Mingsheng Long, Wei Cheng, Xiaoming Jin, Jianmin Wang, Dou Shen.
  In Proceedings of the 10th IEEE International Conference on Data Mining (IEEE ICDM'10). Sydney, Australia, December 2010. (PDF) 
  
  
• Probabilistic Similarity Query on Dimension Incomplete Data,
  Wei Cheng, Xiaoming Jin, Jian-Tao Sun.
  In Proceedings of the 9th IEEE International Conference on Data Mining (IEEE ICDM09). Florida, Miami, December 2009. (PDF)

Research Experience

• Senior Research Staff Member: NEC Laboratories America, 2015.8-now.
• Research Assistant: Data Mining, Bioinformatics, with Prof. Wei Wang, University of North Carolina at Chapel Hill, Fall 2010-Summer 2015.
• Research Intern: IBM Almaden Research, in Machine Learning Systems, Algorithms and Applications Group, with Dr. Alexandre V Evfimievski. 2014.6-2014.9
• Research Intern: Tencent, Inc., in Machine Learning Group, with Dr. Zhifeng Yang, 2010.3-2010.7.
• Research Intern: Microsoft Research Asia, in Machine Learning Group, with Dr. Jian-Tao Sun, 2008.9-2009.4.

Awards

• Best Paper Award Runner-Up, The Workshop of Trustworthy Learning on Graphs with WWW, 2024
• Best Reviewer Award, ECML/PKDD, 2023.
• Best Paper Award, The Workshop of Artificial Intelligence for Time Series Analysis (IJCAI Workshop), 2023
• NEC Global Innovation Unit Outstanding Value Award, 2022
• NEC Business Contribution Award, Oct 2022
• NEC DX Innovator 100, 2022
• NEC Business Contribution Award, May 2022
• NEC Business Contribution Award, 2021
• Knowl. Inf. Syst. (KAIS) on “Bests of ICDM 2018”
• Knowl. Inf. Syst. (KAIS) on “Bests of ICDM 2017”
• Best Research Paper Runner Up Award, SIGKDD 2016.
• Knowl. Inf. Syst. (KAIS) on “Bests of ICDM 2015”
• Statistical Analysis and Data Mining on “Bests of SDM 2012”
• Jiangsu Software Scholarship, Tsinghua University, 2010.
• First-Class Scholarship, Tsinghua University, 2010.

Tools and Data

• 5000 synthetic time series data(Download)
• Graph regularized Dual lasso (GGD-Lasso)(Download)
• Fast and Robust Group-wise eQTL Mapping(Download)
• geQTL (Download) 

Professional Activities

• Chair: IJCAI'22 WORKSHOP, AI4TS: AI FOR TIME SERIES ANALYSIS, KDD'23 Registration Co-Chair
• Area Chair: AAAI'25, ACL'24, NAACL'24, AAAI'24, EMNLP'23, AAAI'23, AAAI'21, WSDM'20, AAAI'20
• SPC/PC Member or Reviewer: ICML'24, SIGIR'24, WSDM'24, CIKM'24, ECMK'24, NeurIPS'23, ICML'23, SIGIR'23, WSDM'23, NeurIPS'22, ICML'22, CIKM'22, SIGKDD'22, SIGIR'22, ICDE'22, ICLR'22, AAAI'22, SDM'22, WSDM'22, ICML'21, KDD'21, SIGIR'21, ICLR'21, CIKM'21, NeurIPS'20, KDD'20, SIGIR'20, CIKM'20, AAAI'20, WSDM'20, AAAI'19, CIKM'18, ICDM'17, SDM'17, IJCAI'15, KDD'14, ECML PKDD'15,'16
• Journal Reviewer: Nature Genetics, TPAMI, Artificial Intelligence, IEEE Transactions on Artificial Intelligence, TMLR, TNNLS, ACM Computing Surveys, TKDE, TKDD, Neurocomputing, IEEE Transactions on Cybernetics

Teaching

• Instructor:COMP 110-01: Introduction to Programming