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 Research Staff Member in Data Science Department at NEC Laboratories America. Here is my CV.

Research Interests

• Data Science: Large-scale Data Mining, Time Series Analysis, System Anomaly Detection, Recommendation
• Machine Learning: Deep Learning, Graph Representation Learning, Transfer Learning, Matrix Factorization
• Web Applications: Text Representation Learning
• 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

• 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)

• 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)

• 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.

• 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)

• 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)

• 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)

• 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)

• 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)
  
  
• 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.

• 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 It's 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)
  
  
• 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)
  
  
• 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)
  
  
• 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 Poceedings 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)
  
  
• 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 Poceedings 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

• 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

• Area Chair: AAAI'21
• SPC/PC Member or Reviewer: ICML'21, KDD'21, SIGIR'21, ICLR'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, TNNLS, ACM Computing Surveys, TKDE, TKDD, Neurocomputing, IEEE Transactions on Cybernetics

Teaching

• Instructor:COMP 110-01: Introduction to Programming