个人简介
教育背景
2016-08--2017-08 Carnegie Mellon University 访问学者（visiting researcher）
2006-09--2007-12 University of California, Los Angeles (UCLA) 访问研究
2005-08--2010-07 清华大学 博士学位、硕士学位
2001-08--2005-06 西安电子科技大学 学士学位
教授课程
大数据分析
科研活动
2013年至今，中文信息学会“社会媒体处理（SMP，Social Media Processing）”专委会委员
2011年被推选为美国加州大学洛杉矶分校（UCLA）电子工程系学术界校友代表
程序委员会委员：AAAI，IJCAI, NIPS, ECML-PKDD, ICDM, 社会媒体处理大会SMP, 中文信息检索会议CCIR
同行评审：IEEE Transactions on Knowledge and Data Engineering(TKDE), ACM Transactions on Knowledge Discovery from Data(TKDD), Journal of Computer Science and Technology (JCST), Chinese Journal of Computers，计算机学报等等。
科研项目
（ 1 ） 社交网络分析与网络信息传播的基础研究, 参与, 国家级, 2013-01--2017-12
（ 2 ） 考虑用户浏览行为的网络短文本推荐的研究, 主持, 国家级, 2013-01--2015-12
（ 3 ） 在线社会媒体中用户观点传播的建模与预测研究, 主持, 国家级, 2016-01--2016-12
（ 4 ） 在线社会关系网络中消息流行度的建模与预测研究, 参与, 国家级, 2015-01--2018-12
（ 5 ） 网络群体观点的形成与动力学建模, 主持, 省级, 2017-01--2019-12
（ 6 ） 大规模多属性图中的异常模式挖掘, 主持, 国家级, 2018-01--2021-12
（ 7 ） 带有时域属性的超大规模异构网络中群体欺诈行为检测研究, 主持, 院级, 2020-05--2021-05
（ 8 ） 基于工业大数据的离散智能制造基础理论与关键技术研究, 参与, 国家级, 2020-01--2023-12
（ 9 ） 地球大数据科学工程：大数据云服务平台项目课题四大数据计算与处理系统研发, 参与, 部委级, 2018-01--2022-12
（ 10 ） 群智开放创新平台及智能推荐系统, 主持, 国家级, 2020-01--2020-12
授权专利
[1] 基于开放知识库的短文本语义概念自动化扩展方法及系统，申请日期：3/14/2013，申请号：ZL 201310081984.6，授权日：4/1/2015，授权号：CN 103150382B，发明人：程学旗，刘盛华，肖永磊，王元卓，刘悦
[2] 面向网络流式数据的事件实时过滤方法和系统，申请日期：4/19/2013，申请号：201310136896.1，授权日期：5/27/2015，授权号：CN103198146A，发明人：程学旗，刘盛华，邱文一，王元卓，刘悦，莫溢，黄展坤
[3] 一种短文本数据的事件演化分析方法，申请日期：3/15/2013，申请号：201310082990.3，授权日期：2015年7月29日，授权号：CN103150383A，发明人：程学旗，刘盛华，李福鑫，王元卓，刘悦
[4] 社会标签自动标注的方法以及社会标签自动标注器，申请日期：9/7/2011，申请号：ZL 201110263798.5，授权日：02/14/2016，授权号：CN 102289514A，发明人：刘盛华，程学旗，郭嘉丰，刘悦，廖华明，朱亚涛
[5] "DYNAMIC PUSH FOR TOPOLOGICAL ROUTING OF SEMICONDUCTOR PACKAGES", Guoqiang Chen, Kaushik Sheth, Egino Sarto, and Shenghua Liu, Filing Date: June 6, 2008, Patent number 8006216, Issue date Aug. 23, 2011. (U.S. patent, issued )
[6] 一个快速的集成电路可布性分析方法，申请日期：6/15/2006，申请号：ZL 00610012271.4，授权日期：7/23/2008，授权号：CN 100405379C，发明人：洪先龙，经彤，刘盛华，许静宇
[7] 社会媒体事件的动态观点演变的可视化方法及设备，申请日期：4/18/2013，申请号：201310134433.1，发明人：程学旗，刘盛华，朱亚涛，王元卓，刘悦，朱文君
近期论文
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Shenghua Liu, Bryan Hooi, and Christos Faloutsos, "HoloScope: Topology-and-Spike Aware Fraud Detection," In Proc. of ACM International Conference on Information and Knowledge Management (CIKM), Singapore, 2017, pp.1539-1548. [ paper ] [ slides ] [ code ]
Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng, " SpecGreedy: Unified Dense Subgraph Detection ", In proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Belgium, Sept 2020. (Best student DM paper award).
Yiwei Wang, Shenghua Liu, Minji Yoon, Hemank Lamba, Wei Wang, Christos Faloutsos, and Bryan Hooi, Provably Robust Node Classification via Low-Pass Message Passing, In proc. of IEEE International Conference on Data Mining (ICDM), Sorrento, Italy, November 17-20, 2020.
Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng. " FlowScope: Spotting Money Laundering Based on Graphs ," Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020. [ paper ] [ code ] [ slides ]
Shenghua Liu, Bryan Hooi, Christos Faloutsos, A Contrast Metric for Fraud Detection in Rich Graphs, IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol 31, Issue 12, Dec. 1 2019, pp. 2235-2248. [ paper ]
Shenghua Liu , Huawei Shen, Houdong Zheng, Xueqi Cheng, Xiangwen Liao, "CT LIS: Learning Influences and Susceptibilities through Temporal Behaviors", ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 13, No. 6, Article 57, 2019. [ paper ]
Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng, " SpecGreedy: Unified Dense Subgraph Detection ", In proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Belgium, Sept 2020. (verified on 40 real-world networks, and a 1.47-billion-edge graph)
Wenjie Feng, Shenghua Liu, Christos Faloutsos, Bryan Hooi, Huawei Shen, Xueqi Cheng, Beyond outliers and on to micro-clusters: Vision-guided anomaly detection, In Proc. of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), 2019, Macau, China, pp541-554. [ paper ]
Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye. BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series, International Joint Conference on Artificial Intellince (IJCAI) 2019, pp4433-4439. [ paper ] [ code ]
Wenjie Feng, Shenghua Liu, Huawei Shen, and Xueqi Cheng, CatchCore: Catching Hierarchical Dense Subtensor, In Proc. of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019), 2019. CatchCore [ paper ] [ code ]
Pudi Chen, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, Xueqi Cheng. NeuCast: Seasonal Neural Forecast of Power Grid Time Series, the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI-18), Stockholm, Sweden, July 13-19, 2018. [ paper ] [ code ]
Bryan Hooi, Kijung Shin, Shenghua Liu, Christos Faloutsos, SMF: Drift-Aware Matrix Factorization with Seasonal Patterns, In Proc. of the SIAM International Conference on Data Mining (SDM19), 2019. [ paper ]
Bryan Hooi, Shenghua Liu, Asim Smailagic, and Christos Faloutsos, BEATLEX: Summarizing and Forecasting Time Series with Patterns, The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Skopje, Macedonia, 2017. [ paper ] [ code ]
Wenjian Yu, Yu Gu, Jian Li, Shenghua Liu, and Yaohang Li: Single-pass PCA of large high-dimensional data, in Proc. the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Austrila, Aug. 2017, pp.3350-3356. [ paper ] [ code ]
Xiaohui Yan, Jiafeng Guo, Shenghua Liu, Xueqi Cheng, Learning Topics in Short Texts by Non-negative Matrix Factorization on Term Correlation Matrix, In Proc. of the SIAM International Conference on Data Mining (SDM), 2013: 749-757. [ paper ]
Yongqing Wang, Huawei Shen, Shenghua Liu, Jinhua Gao and Xueqi Cheng. Cascade dynamics modeling with attention-based recurrent neural network, in Proc. the 26th International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia, 2017, pp.2985-2991. [ paper ]
Yongqing Wang, Huawei Shen, Shenghua Liu and Xueqi Cheng. Learning user-specific latent influence and susceptibility from information cascades. The 29th AAAI Conference on Artificial Intelligence (AAAI-15). Austin, USA, 2015: 477-483. [ paper ]
Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin and Xueqi Cheng, Predict Anchor Links across Social Networks via an Embedding Approach, In Proc. of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 1823-1829, July 9-15, New York City, New York, USA. [ paper ]
Shenghua Liu, Houdong Zheng, Xiangwen Liao, Shenghua Wei, and Xueqi Cheng: Learning Concise Representations of Users' Influences through Online Behaviors, in Proc. the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Austrila, Aug. 2017, pp.2351-2357. [ paper ]
Shenghua Liu, Xueqi Cheng, Fuxin Li, and Fangtao Li, TASC: Topic-Adaptive Sentiment Classification on Dynamic Tweets, IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 27, No. 6, June 2015, pp. 1696-1709. [ paper ]
Shenghua Liu, Fuxin Li, Fangtao Li, Xueqi Cheng and Huawei Shen, Adaptive Co-Training SVM for Sentiment Classification on Tweets, In Proc. of ACM International Conference on Information and Knowledge Management (CIKM), 2013: 2079-2088. [ paper ]
Shenghua Liu, Xueqi Cheng, Fangtao Li, Ranking Tweets by Labeled and Collaboratively Selected Pairs with Transitive Closure, In Proc. of Twenty-Eighth Conference on Artificial Intelligence (AAAI-14), 2014: 1235-1241. [ paper ]
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