Philip S. Yu

Philip S. Yu's main research interests include big data, data mining (especially on graph/network mining), social network, privacy preserving data publishing, data stream, database systems, and Internet applications and technologies. He is a Disthinguished Professor in the Department of Computer Science at UIC and also holds the Wexler Chair in Information and Technology. Before joining UIC, he was with IBM Thomas J. Watson Research Center, where he was manager of the Software Tools and Techniques department. Dr. Yu has published more than 970 papers in refereed journals and conferences with more than 74,500 citations and an H-index of 127. He holds or has applied for more than 300 US patents.

Dr. Yu is a Fellow of the ACM and the IEEE. He is the recepient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society's 2013 Technical Achievement Award for "pioneering and fundamentally innovative contributions to scalable indexing, querying, searching, mining and anonymization of big data", and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. He also received an IEEE Region 1 Award for "promoting and perpetuating numerous new electrical engineering concepts" in 1999. He had received several UIC honors, including Research of the Year at 2013 and UI Faculty Scholar at 2014. He also received many IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 94th plateau of Invention Achievement Awards. He was an IBM Master Inventor.

Dr. Yu is the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data. He is on the steering committee of ACM Conference on Information and Knowledge Management and was a steering committee member of the IEEE Conference on Data Mining and the IEEE Conference on Data Engineering. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He had also served as an associate editor of ACM Transactions on the Internet Technology (2000-2010) and Knowledge and Information Systems (1998-2004). In addition to serving as program committee member on various conferences, he was the program chair or co-chairs of the 2009 IEEE Intl. Conf. on Service-Oriented Computing and Applications, the IEEE Workshop of Scalable Stream Processing Systems (SSPS'07), the IEEE Workshop on Mining Evolving and Streaming Data (2006), the 2006 joint conferences of the 8th IEEE Conference on E-Commerce Technology (CEC' 06) and the 3rd IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE' 06), the 11th IEEE Intl. Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,the 2nd IEEE Intl. Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases, and the 2nd IEEE Intl. Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair or co-chairs of the 2016 IEEE Intl. Conference on BIGDATA, the 2014 IEEE Intl. Conference on Data Science and Advanced Analytics, the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, the 2012 Pacific-Asia Conference on Knowledge Discovery and Data Mining, the 2009 IEEE Intl. Conf. on Data Mining, the 2009 IEEE Intl. Conf. on Data Engineering, the 2006 ACM Conference on Information and Knowledge Management, the 1998 IEEE Intl. Conference on Data Engineering, and the 2nd IEEE Intl. Conference on Data Mining.

Dr. Yu received the B.S. Degree in E.E. from National Taiwan University, the M.S. and Ph.D. degrees in E.E. from Stanford University, and M.B.A. degree from New York University.

Related websites:

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA

AMINER

BOOKS

Conference Publications

1. Multi-Hot Compact Network Embedding. Chaozhuo Li, Lei Zheng, Senzhang Wang, Feiran Huang, Philip S. Yu, Zhoujun Li. [CIKM 2019]

2. Partially Shared Adversarial Learning For Semi-supervised Multi-platform User Identity Linkage. Chaozhuo Li, Senzhang Wang, Yanbo Liang, Philip S. Yu, Zhoujun Li. [CIKM 2019]

3. Multi-grained Named Entity Recognition. Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu. [ACL 2019]

4. Joint Slot Filling and Intent Detection via Capsule Neural Networks. Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip Yu. [ACL 2019]

5. Deep Distribution Network: Addressing the Data Sparsity Issue for Top-N Recommendation. Lei Zheng, Chaozhuo Li, Chun-Ta Lu, Jiawei Zhang, Philip S. Yu. [SIGIR 2019]

6. Gated Spectral Units: Modeling Co-evolving Patterns for Sequential Recommendation. Lei Zheng, Ziwei Fan, Chun-Ta Lu, Jiawei Zhang, Philip S. Yu. [SIGIR 2019]

7. Generative Dual Adversarial Network for Generalized Zero-shot Learning. He Huang, Changhu Wang, Philip S. Yu, Chang-Dong Wang. [CVPR 2019]

8. Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce. Jianguo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Xiuming Pan, Yu Gong, Philip S. Yu. [NAACL-HLT 2019]

9. Joint Training for Review Reading Comprehension and Aspect-based Sentiment Analysis. Hu Xu, Bing Liu, Lei Shu, Philip Yu. [NAACL-HLT 2019]

10. Heterogeneous Graph Matching Networks for Unknown Malware Detection. Shen Wang, Zhengzhang Chen, Xiao Yu, Ding Li, Jingchao Ni, Lu-An Tang, Jiaping Gui, Zhichun Li, Haifeng Chen, Philip S. Yu. [IJCAI 2019]

11. Outlier-Robust Multi-Aspect Streaming Tensor Completion and Factorization. Mehrnaz Najafi, Lifang He, Philip S. Yu. [IJCAI 2019]

12. Private Model Compression via Knowledge Distillation. Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, Philip S. Yu. [AAAI 2019]

13. Adversarial Learning forWeakly-Supervised Social Network Alignment. Chaozhuo Li, Senzhang Wang, Yukun Wang, Philip S. Yu, Yanbo Liang, Yun Liu, Zhoujun Li. [AAAI 2019]

14. Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification. Shen Wang, Zhengzhang Chen, Ding Li, Lv-An Tang, Jingchao Ni, Zhichun Li, Junghwan Rhee, Haifeng Chen, Philip S Yu. [SDM 2019]

15. Spectral Collaborative Filtering. Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, Philip Yu. [RecSys 2018]

16. A Self-Organizing Tensor Architecture for Multi-View Clustering. Lifang He, Chun-Ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, and Fei Wang. [ICDM 2018]

17. dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction. He Huang, Bokai Cao, Philip S. Yu, Chang-Dong Wang, and Alex D. Leow. [ICDM 2018]

18. FI-GRL: Fast Inductive Graph Representation Learning via Projection-Cost Preservation. Fei Jiang, Lei Zheng, Jin Xu, and Philip S. Yu. [ICDM 2018]

19. Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction. Jianguo Zhang*, Ji Wang*, Lifang He, Zhao Li, and Philip S. Yu. [ICDM 2018]

20. SSDMV: Semi-supervised Deep Social Spammer Detection by Multi-View Data Fusion. Chaozhuo Li, Senzhang Wang, Lifang He, Philip S. Yu, Yanbo Liang, Zhoujun Li. [ICDM 2018]

21. Distribution Distance Minimization for Unsupervised User Identity Linkage. Chaozhuo Li, Senzhang Wang, Philip S. Yu, Lei Zheng, Xiaoming Zhang, Zhoujun Li, Yanbo Liang. [CIKM 2018]

22. Zero-shot User Intent Detection via Capsule Neural Networks. Congying Xia*, Chenwei Zhang*, Xiaohui Yan, Yi Chang, Philip S. Yu. [EMNLP 2018]

23. Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction. Hu Xu, Bing Liu, Lei Shu, Philip S. Yu. [ACL 2018]

24. On the Generative Discovery of Structured Medical Knowledge, Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu. [KDD 2018]

25. Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud, Ji Wang*, Jianguo Zhang*, Weidong Bao, Xiaomin Zhu, Bokai Cao, Philip S. Yu. [KDD 2018]

26. Multi-Round Influence Maximization, Lichao Sun, Weiran Huang, Philip Yu, Wei Chen. [KDD 2018]

27. Lifelong Domain Word Embedding via Meta-Learning, Hu Xu, Bing Liu, Lei Shu, Philip Yu. [IJCAI 2018]

28. On Spectral Graph Embedding: a Non-backtracking Perspective and Graph Approximation, Fei Jiang, Lifang He, Yi Zheng, Enqiang Zhu, Jin Xu and Philip S. Yu. [SDM 2018]

29. Multi-Task Pharmacovigilance Mining from Social Media Posts, Shaika Chowdhury, Chenwei Zhang and Philip S. Yu. [WWW 2018]

30. On Exploring Semantic Meanings of Links for Embedding Social Networks, Linchuan Xu, Xiaokai Wei, Jiannong Cao and Philip S. Yu. [WWW 2018]

31. Learning from Multi-View Multi-Way Data via Structural Factorization Machines, Chun-Ta Lu, Lifang He, Hao Ding, Bokai Cao and Philip S. Yu. [WWW 2018]

32. Dual Attention Network for Product Compatibility and Function Satisfiability Analysis, Hu Xu, Sihong Xie, Shu Lei and Philip S. Yu. [AAAI 2018]

33. Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis, Ye Liu, Lifang He, Bokai Cao, Philip S. Yu, Ann B. Ragin and Alex D. Leow. [AAAI 2018]

34. MNE: Emerging Network Embedding with Aligned Autoencoder, Jiawei Zhang, Congying Xia, Chenwei Zhang, Limeng Cui, Yanjie Fu and Philip S. Yu. [ICDM 2017]

35. HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks, Bokai Cao, Mia Mao, Siim Viidu and Philip S. Yu. [ICDM 2017]

36. Collaborative Inference of Coexisting Information Diffusions, Yanchao Sun, Cong Qian, Ning Yang and Philip S. Yu. [ICDM 2017]

37. Multi-view Graph Embedding with Hub Detection for Brain Network Analysis, Guixiang Ma, Chun-Ta Lu, Lifang He, Philip S. Yu and Ann B. Ragin. [ICDM 2017]

38. A Broad Learning Approach for Context-Aware Mobile Application Recommendation, Tingting Liang, Lifang He, Chun-Ta Lu, Liang Chen, Philip S. Yu and Jian Wu. [ICDM 2017]

39. ECD: Enterprise Social Community Detection via Hierarchical Structure Fusion, Jiawei Zhang, Limeng Cui, Philip S. Yu, Yuanhua Lv and Yanjie Fu. [CIKM 2017]

40. Multi-Source Collaborative Recommendation, Junxing Zhu, Jiawei Zhang, Lifang He, Quanyuan Wu, Bin Zhou, Chenwei Zhang and Philip S. Yu. [CIKM 2017]

41. Unsupervised Feature Selection with Heterogeneous Side Information, Xiaokai Wei, Bokai Cao and Philip S. Yu. [CIKM 2017]

42. Multi-view Clustering via Graph Embedding for Connectome Analysis, Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow and Ann B. Ragin. [CIKM 2017]

43. Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services, Yuqi Wang, Jiannong Cao, Lifang He, Wengen Li, Lichao Sun and Philip S. Yu. [CIKM 2017]

44. Structural Deep Brain Network Mining, Shen Wang, Lifang He, Bokai Cao, Chun-Ta Lu, Philip S. Yu and Ann B. Ragin. [KDD 2017]

44. DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection, Bokai Cao, Lei Zheng, Chenwei Zhang, Philip S. Yu, Andrea Piscitello, John Zulueta, Olu Ajilore, Kelly Ryan and Alex D. Leow. [KDD 2017]

45. Kernalized Support Tensor Machines, Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu and Ann B. Ragin. [ICML 2017]

46. Multi-way Multi-level Kernel Modeling for Neuroimaging Classification, Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, Linlin Shen, Philip S. Yu and Ann B. Ragin. [CVPR 2017]

47. SEVEN: Deep Semi-supervised Verification Networks, Vahid Noroozi, Lei Zheng, Sara Bahaadini, Sihong Xie and Philip S. Yu. [IJCAI 2017]

48. t-BNE: Tensor-based Brain Network Embedding, Bokai Cao, Lifang He, Xiaokai Wei, Mengqi Xing, Philip S. Yu, Heide Klumpp and Alex D. Leow. [SDM 2017]

49. Link Prediction across Aligned Networks with Sparse Low Rank Matrix Estimation, Jiawei Zhang, Jianhui Chen, Shi Zhi, Yi Chang, Philip S. Yu and Jiawei Han. [ICDE 2017]

50. Enterprise Social Community Detection, Jiawei Zhang, Philip S. Yu and Yuanhua Lv. [ICDE 2017]

51. Cross View Link Prediction by Learning Noise-resilient Representation Consensus, Xiaokai Wei, Linchuan Xu, Bokai Cao and Philip S. Yu. [WWW 2017]

52. Embedding of Embedding (EOE) : Embedding for Coupled Heterogeneous Networks, Linchuan Xu, Xiaokai Wei, Jiannong Cao and Philip S. Yu. [WSDM 2017]

53. Enterprise Employee Training via Project Team Formation, Jiawei Zhang, Philip S. Yu and Yuanhua Lv. [WSDM 2017]

54. Joint Deep Modeling of Users and Items Using Reviews for Recommendation, Lei Zheng, Vahid Noroozi and Philip S. Yu. [WSDM 2017]

55. Link Prediction with Cardinality Constraint, Jiawei Zhang, Jianhui Chen, Junxing Zhu, Yi Chang and Philip S. Yu. [WSDM 2017]

56. Multilinear Factorization Machines for Multi-Task Multi-View Learning, Chun-Ta Lu, Lifang He, Weixiang Shao, Bokai Cao and Philip S. Yu. [WSDM 2017]

57. Online Unsupervised Multi-view Feature Selection, Weixiang Shao, Lifang He, Chun-Ta Lu, Xiaokai Wei and Philip S. Yu. [ICDM 2016]

58. Information Diffusion at Workplace, Jiawei Zhang, Philip S. Yu, Yuanhua Lv and Qianyi Zhan. [CIKM 2016]

59. Multi-source Hierarchical Prediction Consolidation, Chenwei Zhang, Sihong Xie, Yaliang Li, Jing Gao, Wei Fan and Philip S. Yu. [CIKM 2016]

60. Active Zero-shot Learning, Sihong Xie, Shaoxiong Wang and Philip S. Yu. [CIKM 2016]

61. Efficient Hidden Trajectory Reconstruction from Sparse Data, Ning Yang and Philip S. Yu. [CIKM 2016]

62. Enhancing Traffic Congestion Estimation with Social Media by Coupled Hidden Markov Model, Senzhang Wang, Fengxiang Li, Leon Stenneth and Philip S. Yu. [ECML/PKDD 2016]

63. Multi-Graph Clustering based on Interior-Node Topology with Applications to Brain Networks, Guixiang Ma, Lifang He, Bokai Cao, Jiawei Zhang and Philip S. Yu. [ECML/PKDD 2016]

64. Semi-supervised Tensor Factorization for Brain Network Analysis, Bokai Cao, Chun-Ta Lu, Xiaokai Wei, Philip S. Yu and Alex D. Leow. [ECML/PKDD 2016]

65. Trust Hole Identification in Signed Networks, Jiawei Zhang, Qianyi Zhan, Lifang He, Charu Aggarwal and Philip S. Yu. [ECML/PKDD 2016]

66. Joint Community and Structural Hole Spanner Detection via Harmonic Modularity, Lifang He, Chun-Ta Lu, Jiaqi Ma, Jianping Cao, Linlin Shen and Philip S. Yu. [KDD 2016]

67. Item Recommendation for Emerging Online Businesses, Chun-Ta Lu, Sihong Xie, Weixiang Shao, Lifang He and Philip S. Yu. [IJCAI 2016]

68. Understanding Information Diffusion under Interactions, Yuan Su, Xi Zhang, Philip S. Yu, Wen Hua, Xiaofang Zhou and Binxing Fang. [IJCAI 2016]

69. Identifying Connectivity Patterns for Brain Diseases Via Multi-Side-View Guided Deep Architectures, Jingyuan Zhang, Bokai Cao, Sihong Xie, Chun-Ta Lu, Philip S. Yu and Ann B. Ragin. [SDM 2016]

70. Effective Crowd Expertise Modeling Via Cross Domain Sparsity and Uncertainty Reduction, Sihong Xie, Qingbo Hu, Weixiang Shao, Jingyuan Zhang, Jing Gao, Wei Fan and Philip S. Yu. [SDM 2016]

71. Nonlinear Joint Unsupervised Feature Selection, Xiaokai Wei, Bokai Cao and Philip S. Yu. [SDM 2016]

72. Spatio-Temporal Tensor Analysis for Whole-Brain Fmri Classification, Guixiang Ma, Lifang He, Chun-Ta Lu, Philip S. Yu, Linlin Shen and Ann B. Ragin. [SDM 2016]

73. Mining Online Social Data for Detecting Social Netowrk Mental Disorders, Hong-Han Shuai, Chih-Ya Shen, De-Nian Yang, Yi-Feng Lan, Wang-Chien Lee, Philip S. Yu and Ming-Syan Chen. [WWW 2016]

74. PCT: Partial Co-Alignment of Social Networks, Jiawei Zhang and Philip S. Yu. [WWW 2016]

75. HeteroSales: Utilizing Heterogeneous Social Networks to Identify the Next Enterprise Customer, Qingbo Hu, Sihong Xie, Jiawei Zhang, Qiang Zhu, Songtao Guo and Philip S. Yu. [WWW 2016]

76. Mining User Intentions from Medical Queries: A Neural Network Based Heterogeneous Jointly Modeling Approach, Chenwei Zhang, Wei Fan, Nan Du and Philip S. Yu. [WWW 2016]

77. Multi-view Machines, Bokai Cao, Hucheng Zhou, Guoqiang Li and Philip S. Yu. [WSDM 2016]

78. Unsupervised Feature Selection on Networks: A Generative View, Xiaokai Wei, Bokai Cao and Philip S. Yu. [AAAI 2016]

79. Multiple Anonymized Social Networks Alignment, Jiawei Zhang and Philip S. Yu. [ICDM 2015]

80. Mining Brain Networks Using Multiple Side Views for Neurological Disorder Identification, Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu and Ann B. Ragin. [ICDM 2015]

81. Ensemble of Diverse Sparsifications for Link Prediction in Large-Scale Networks, Yi-Ling Chen, Ming-Syan Chen and Philip S. Yu. [ICDM 2015]

82. Learning Entity Types from Query Logs via Graph-Based Modeling, Jingyuan Zhang, Luo Jie, Altaf Rahman, Sihong Xie, Yi Chang and Philip S. Yu. [CIKM 2015]

83. Enterprise Social Link Recommendation, Jiawei Zhang, Yuanhua Lv and Philip S. Yu. [CIKM 2015]

84. Forming Online Support Groups for Internet and Behavior Related Addictions, Chih-Ya Shen, Hong-Han Shuai, De-Nian Yang, Yi-Feng Lan, Wang-Chien Lee, Philip S. Yu and Ming-Syan Chen. [CIKM 2015]

85. Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks, Chuan Shi, Zhiqiang Zhang, Ping Luo, Philip S. Yu, Yading Yue and Bin Wu. [CIKM 2015]

86. Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization, Weixiang Shao, Lifang He and Philip S. Yu. [ECML/PKDD 2015]

87. Discovering Audience Groups and Group-Specific Influencers, Shuyang Lin, Qingbo Hu, Jingyuan Zhang and Philip S. Yu. [ECML/PKDD 2015]

88. Organizational Chart Inference, Jiawei Zhang, Philip S. Yu and Yuanhua Lv. [KDD 2015]

89. Integrated Anchor and Social Link Predictions across Social Networks, Jiawei Zhang and Philip S. Yu. [IJCAI 2015]

90. Efficient Partial Order Preserving Unsupervised Feature Selection on Networks, Xiaokai Wei, Sihong Xie and Philip S. Yu. [SDM 2015]

91. Community Detection for Emerging Networks, Jiawei Zhang and Philip S. Yu. [SDM 2015]

92. Frameworks to Encode User Preferences for Inferring Topic-sensitive Information Networks, Qingbo Hu, Sihong Xie, Shuyang Lin, Wei Fan and Philip S. Yu. [SDM 2015]

93. OnlineCM: Real-time Consensus Classification with Missing Values, Bowen Dong, Sihong Xie, Jing Gao, Wei Fan and Philip S. Yu. [SDM 2015]

94. Predicting Neighbor Distribution in Heterogeneous Information Networks, Yuchi Ma, Ning Yang, Chuan Li, Lei Zhang and Philip S. Yu. [SDM 2015]

95. Burst Time Prediction in Cascades, Senzhang Wang, Zhao Yan, Xia Hu, Philip S. Yu and Zhoujun Li. [AAAI 2015]

Journal Publications

1. Info-Trust: A Multi-Criteria and Adaptive Trustworthiness Calculation Mechanism for Information Sources. Yali Gao, Xiaoyong Li, Jirui Li, Yunquan Gao, Philip S. Yu. IEEE Access 7: 13999-14012 (2019)

2. Coupled Tensor Decomposition for User Clustering in Mobile Internet Traffic Interaction Pattern. Ke Yu, Lifang He, Philip S. Yu, Wenkai Zhang, Yue Liu. IEEE Access 7: 18113-18124 (2019)

3. A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering. Ling Huang, Chang-Dong Wang, Hong-Yang Chao, Jian-Huang Lai, Philip S. Yu. IEEE Access 7: 19550-19563 (2019)

4. Mining Spatiotemporal Diffusion Network: A New Framework of Active Surveillance Planning. Hechang Chen, Bo Yang, Jiming Liu, Xiao-Nong Zhou, Philip S. Yu. IEEE Access 7: 108458-108473 (2019)

5. Multi-view collective tensor decomposition for cross-modal hashing. Limeng Cui, Jiawei Zhang, Lifang He, Philip S. Yu. IJMIR 8(1): 47-59 (2019)

6. Deep Latent Factor Model with Hierarchical Similarity Measure for recommender systems. Jiayu Han, Lei Zheng, He Huang, Yuanbo Xu, Philip S. Yu, Wanli Zuo. Inf. Sci. 503: 521-532 (2019)

7. Correlated utility-based pattern mining. Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, Hamido Fujita, Philip S. Yu. Inf. Sci. 504: 470-486 (2019)

8. Integrated anchor and social link predictions across multiple social networks. Qianyi Zhan, Jiawei Zhang, Philip S. Yu. Knowl. Inf. Syst. 60(1): 303-326 (2019)

9. Community detection using multilayer edge mixture model. Knowl. Han Zhang, Chang-Dong Wang, Jian-Huang Lai, Philip S. Yu. Inf. Syst. 60(2): 757-779 (2019)

10. Adversarial learning for multi-view network embedding on incomplete graphs. Chaozhuo Li, Senzhang Wang, Dejian Yang, Philip S. Yu, Yanbo Liang, Zhoujun Li. Knowl.-Based Syst. 180: 91-103 (2019)

11. An unsupervised parameter learning model for RVFL neural network. Yongshan Zhang, Jia Wu, Zhihua Cai, Bo Du, Philip S. Yu. Neural Networks 112: 85-97 (2019)

12. Classification, Denoising, and Deinterleaving of Pulse Streams With Recurrent Neural Networks. Zhang-Meng Liu, Philip S. Yu. IEEE Trans. Aerospace and Electronic Systems 55(4): 1624-1639 (2019)

13. Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering. Chang-Dong Wang, Zhi-Hong Deng, Jian-Huang Lai, Philip S. Yu. IEEE Trans. Cybernetics 49(7): 2678-2692 (2019)

14. Efficient Traffic Estimation With Multi-Sourced Data by Parallel Coupled Hidden Markov Model. Senzhang Wang, Xiaoming Zhang, Fengxiang Li, Philip S. Yu, Zhiqiu Huang. IEEE Trans. Intelligent Transportation Systems 20(8): 3010-3023 (2019)

15. Feature Selection via Transferring Knowledge Across Different Classes. Zheng Wang, Xiaojun Ye, Chaokun Wang, Philip S. Yu. TKDD 13(2): 22:1-22:29 (2019)

16. A Survey of Parallel Sequential Pattern Mining. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Philip S. Yu. TKDD 13(3): 25:1-25:34 (2019)

17. Heterogeneous Information Network Embedding for Recommendation. Chuan Shi, Binbin Hu, Wayne Xin Zhao, Philip S. Yu. IEEE Trans. Knowl. Data Eng. 31(2): 357-370 (2019)

18. IAD: Interaction-Aware Diffusion Framework in Social Networks. Xi Zhang, Yuan Su, Siyu Qu, Sihong Xie, Binxing Fang, Philip S. Yu. IEEE Trans. Knowl. Data Eng. 31(7): 1341-1354 (2019)

19. An Attention-augmented Deep Architecture for Hard Drive Status Monitoring in Large-scale Storage Systems. Ji Wang, Weidong Bao, Lei Zheng, Xiaomin Zhu, Philip S. Yu. TOS 15(3): 21:1-21:26 (2019)

20. Deep Dynamic Network Embedding for Link Prediction. Taisong Li, Jiawei Zhang, Philip S. Yu, Yan Zhang, Yonghong Yan. IEEE Access 6: 29219-29230 (2018)

21. Stock Market Prediction via Multi-Source Multiple Instance Learning. Xi Zhang, Siyu Qu, Jieyun Huang, Binxing Fang, Philip S. Yu. IEEE Access 6: 50720-50728 (2018)

22. Improving stock market prediction via heterogeneous information fusion. Xi Zhang, Yunjia Zhang, Senzhang Wang, Yuntao Yao, Binxing Fang, Philip S. Yu. Knowl.-Based Syst. 143: 236-247 (2018)

23. Broad Learning: An Emerging Area in Social Network Analysis. Jiawei Zhang, Philip S. Yu. SIGKDD Explorations 20(1): 24-50 (2018)

24. QMSampler: Joint Sampling of Multiple Networks with Quality Guarantee. Hong-Han Shuai, De-Nian Yang, Chih-Ya Shen, Philip S. Yu, Ming-Syan Chen. IEEE Trans. Big Data 4(1): 90-104 (2018)

25. Effective Prediction of Missing Data on Apache Spark over Multivariable Time Series. Weiwei Shi, Yongxin Zhu, Philip S. Yu, Jiawei Zhang, Tian Huang, Chang Wang, Yufeng Chen. IEEE Trans. Big Data 4(4): 473-486 (2018)

26. LoPub: High-Dimensional Crowdsourced Data Publication With Local Differential Privacy. Xuebin Ren, Chia-Mu Yu, Weiren Yu, Shusen Yang, Xinyu Yang, Julie A. McCann, Philip S. Yu. IEEE Trans. Information Forensics and Security 13(9): 2151-2166 (2018)

27. Efficient Computation of G-Skyline Groups. Changping Wang, Chaokun Wang, Gaoyang Guo, Xiaojun Ye, Philip S. Yu. IEEE Trans. Knowl. Data Eng. 30(4): 674-688 (2018)

28. A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining. Hong-Han Shuai, Chih-Ya Shen, De-Nian Yang, Yi-Feng Lan, Wang-Chien Lee, Philip S. Yu, Ming-Syan Chen. IEEE Trans. Knowl. Data Eng. 30(7): 1212-1225 (2018)

29. Multiple Structure-View Learning for Graph Classification. Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, Philip S. Yu. IEEE Trans. Neural Netw. Learning Syst. 29(7): 3236-3251 (2018)

30. Differential Privacy and Applications. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu. Advances in Information Security 69, Springer 2017, ISBN 978-3-319-62002-2, pp. 1-222

Books or Proceedings

1. “Broad Learning Through Fusions: An Application on Social Networks”. Zhang, Jiawei, and S. Yu Philip. Springer, 2019.

2. “Behavior Computing”, (Co-Editor with L. Cao), Springer, 2012.

3. 2.”Agents and Data Mining Interaction”, (Co-Editor with L. Cao, A. Bazzan, A. Symeonidis, V. Gorodetsky, and G. Weiss), Springer, 2012. (7th International Workshop on Agents and Data Mining Interation, ADMI 2011, Taipei, Taiwan, May 2-6, 2011, Revised Selected Papers.)

4. ”Domain Driven Data Mining”, (Co-Editor with L. Cao, C. Zhang and Y. Zhao), Springer, 2012.

Book Chapters

1. ”Dimensionality Reduction and Filtering on Time Series Sensor Streams”, (with S. Papadimitiou, J. Sun, and C. Faloutsos), Managing and Mining Sensor Data, ed. by C. Aggarwal, Springer, 2013, pp. 103-141.

2. ”Efficient Direct Mining of Selective Discriminative Patterns for Classification”, (with H. Cheng, J. Han, and X. Yan), Contrast Data Mining: Concepts, Algorithms, and Applications, ed. by G. Dong, J. Bailey, CRC Press, 2013.

3. ”Homogeneous and Heterogeneous Distributed Classification for Pocket Data Mining”, (with F.T. Stahl, M.M. Gaber, P. Aldridge, D. May, H. Liu, and M. Bramer), T. Large-Scale Data- and Knowledge-Centered Systems, Vol. 5, ed. by A. Hameurlain, J. Kung and R. Wagner, Springer, 2012, pp. 183-205.

Ph.D., Stanford University, 1978