基本信息

毛文吉  研究员、博士生导师  中国科学院自动化研究所
电子邮件: wenji.mao@ia.ac.cn
联系电话: 010-82544789
通信地址: 北京中关村东路95号
邮政编码: 100190

研究领域

人工智能,网络大数据分析挖掘,社会计算,情报安全信息学

招生信息

1. [学科专业]:模式识别与智能系统,[研究方向]:人工智能理论与方法
2. [学科专业]:社会计算,[研究方向]:互联网大数据解析

教育背景

2001-09--2006-08   美国南加州大学计算机科学系   获博士学位
1990-09--1993-07   中国科学院数学研究所   获硕士学位
1986-09--1990-07   吉林大学计算机科学与技术系   获学士学位

工作经历

工作简历:

1990-1993年,在陆汝钤院士的指导下,就读于中国科学院数学研究所。参与了国家重点攻关项目(天马)专家系统开发环境的研制。该项目获国家科技进步二等奖和中国科学院科技进步一等奖。

1993-1999年,在中国科学院研究生院计算机学部任职,1996年起任讲师。主持系统开发实验室工作并主讲人工智能相关课程,期间参与了多项国家课题的研究与开发。

1999-2001年,赴德国人工智能研究中心(DFKI)进行合作研究,2001年被聘为DFKI研究科学家。为欧盟四国联合项目SAID和DFKI研究项目Presence的主要研制人员。

2001-2006年,在美国南加州大学 Institute for Creative Technologies(USC/ICT)任研究助理,是美国军方两项重大研究计划MRE和SASO-ST中社会模拟技术的第一研制人。博士工作提出第一个基于认知科学和心理学的社会因果推理计算模型。

2006年9月加入中国科学院自动化研究所,任副研究员。2008年任硕士生导师,2012年至今任研究员、博士生导师。主持多项国家自然科学基金、中科院项目以及与国家核心部门的合作项目。

2014年至今担任互联网大数据与安全信息学研究中心副主任,2015年10月起被聘为中国科学院大学岗位教授。

社会任职:

担任《ACM Computing Surveys》、《IEEE Intelligent Systems》等期刊编委,《软件学报》责任编委,多次应邀主编SCI/SSCI学术期刊专刊和组织本领域国际学术研讨会,任会议主席10余次及100多个国际会议的程序委员会委员或Session主席。曾任ACM北京分会主席、中国人工智能学会理事,现任中国计算机学会大数据专家委委员、中国指控学会大数据科学与工程专委会常委等职。

担任国际期刊审稿人:ACM Computing Surveys、ACM Trans. Intelligent Systems and Technology、Autonomous Agents and Multi-Agent Systems、Expert Systems with Applications、IEEE Computational Intelligence、IEEE Intelligent Systems、IEEE Trans. Affective Computing/Big Data/Computational Social Systems/Knowledge and Data Engineering/Multimedia/Neural Networks and Learning Systems、Information Processing and Management、Information Sciences、Machine Intelligence Research、Neurocomputing、Pattern Recognition、Patterns、Science China Information Sciences、Social Science Computer Review 等

担任国际会议PC/SPC:AAAI、AAMAS、AILA、AMT、CCDM、CCF AI/Bigdata、CCIS、CogSci、ECAI、EUROMEDIA、GAMEON、IEEE ICBK、IEEE ISI、IEEE/WIC/ACM IAT、ICONIP、IIP、IJCAI、IVA、PAAMS、PAISI、PRIMA、SMP、WI 等

专利与奖励

中国自动化学会“科学技术进步奖",一等奖(排名第三)

中国人工智能学会“吴文俊人工智能科学技术创新奖",二等奖(排名第一)

美国南加州大学“杰出学术成就奖",校级(2006)

出版信息

主要发表论文:

[1] X. Zhang, X. Zheng, W. Mao, et al. Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection against Automatic Retrieval Models. IEEE Transactions on Computational Social Systems (TCSS), accepted for publication.

[2] Z. Zeng, N. Xu, W. Mao, et al. An Orthogonal Subspace Decomposition Method for Cross-Modal Retrieval. IEEE Intelligent Systems, 37(3):45-53, 2022.

[3] S. Wang, W. Mao, P. Wei, et al. Knowledge Structure Driven Prototype Learning and Verification for Fact Checking. Knowledge-Based Systems, 238:107910, 2022.

[4] Z. Zeng, Y. Sun and W. Mao. Multimodal Coordinated Clustering Network for Large-Scale Cross-Modal Retrieval. Proceedings of ACM MM, pp.5427-5435, 2021.

[5] Z. Zeng, S. Wang, N. Xu and W. Mao. PAN: Prototype-based Adaptive Network for Robust Cross-Modal Retrieval. Proceedings of SIGIR, pp.1125-1134, 2021.

[6] J. Zhao, P. Wei and W. Mao. Robust Neural Text Classification and Entailment via Mixup Regularized Adversarial Training. Proceedings of SIGIR, pp.1778-1782, 2021.

[7] S. Wang and W. Mao. Modeling Inter-Cliam Interactions for Verifying Multiple Claims.  Proceedings of CIKM, pp.3503-3507, 2021.

[8] X. Zhang, X. Zheng and W. Mao. Adversarial Perturbation Defense on Deep Neural Networks. ACM Computing Surveys (CSUR), 54(8):159, 2021.

[9] P. Wei, J. Zhao and W. Mao. A Graph-to-Sequence Learning Framework for Summarizing Opinionated Texts. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 29:1650-1660, 2021.

[10] N. Xu, W. Mao, P. Wei, et al. MDA: Multimodal Data Augmentation Framework for Boosting Performance on Sentiment/Emotion Classification Tasks. IEEE Intelligent Systems, 36(6):3-12, 2021.

[11] P. Wei, J. Zhao and W. Mao. Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction. Proceedings of ACL, pp.3171-3181, 2020.

[12] N. Xu, Z. Zeng and W. Mao. Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association. Proceedings of ACL, pp.3777-3786, 2020.

[13] Z. Zeng, N. Xu and W. Mao. Event-Driven Network for Cross-Modal Retrieval. Proceedings of CIKM, pp.2297-2300, 2020.

[14] Q. Kong, W. Mao, G. Chen, et al. Exploring Trends and Patterns of Popularity Stage Evolution in Social Media. IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC), 50(10):3817-3827, 2020.

[15] P. Wei, W. Mao and G. Chen. A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection. Proceedings of AAAI, pp.7249-7256, 2019.

[16] N. Xu, W. Mao and G. Chen. Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis. Proceedings of AAAI, pp.371-378, 2019.

[17] P. Wei and W. Mao. Modeling Transferable Topics for Cross-Target Stance Detection. Proceedings of SIGIR, pp.1173-1176, 2019.

[18] P. Wei, N. Xu and W. Mao. Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity. Proceedings of EMNLP, pp.4789-4800, 2019.

[19] J. Lin, Q. Kong, W. Mao, et al. A Topic Enhanced Approach to Detecting Multiple Standpoints in Web Texts. Information Sciences, 501:483-494, 2019.

[20] G. Chen, Q. Kong, N. Xu and W. Mao. NPP: A Neural Popularity Prediction Model for Social Media Content. Neurocomputing, 333:221-230, 2019.

[21] N. Xu, W. Mao and G. Chen. A Co-Memory Network for Multimodal Sentiment Analysis. Proceedings of SIGIR, pp.929-932, 2018.

[22] P. Wei, J. Lin and W. Mao. Multi-Target Stance Detection via a Dynamic Memory-Augmented Network. Proceedings of SIGIR, pp.1229-1232, 2018.

[23] G. Chen, N. Xu and W. Mao. An Encoder-Memory-Decoder Framework for Sub-Event Detection in Social Media. Proceedings of CIKM, pp.1575-1578, 2018.

[24] N. Xu, G. Chen and W. Mao. MNRD: A Merged Neural Model for Rumor Detection in Social Media. Proceedings of IJCNN, pp.885-891, 2018.

[25] P. Wei, W. Mao and D. Zeng. A Target-Guided Neural Memory Model for Stance Detection in Twitter. Proceedings of IJCNN, pp. 2068-2075, 2018.

[26] 林俊杰, 王磊, 毛文吉. 面向社会事件的半监督自训练多方立场分析. 模式识别与人工智能, 31(12):1074−1084, 2018.

[27] J. Lin, W. Mao and D. Zeng. Personality-based Refinement for Sentiment Classification in Microblog. Knowledge-Based Systems, 132:204-214, 2017.

[28] N. Xu and W. Mao. MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis. Proceedings of CIKM, pp.2399-2402, 2017.

[29] J. Lin, W. Mao and Y. Zhang. An Enhanced Topic Modeling Approach to Multiple Stance Identification. Proceedings of CIKM, pp.2167-2170, 2017.

[30] Y. Zhang, W. Mao and J. Lin. Dynamic Topic Modeling in Short Texts. Proceedings of IEEE ICBK, pp.315-319, 2017.

[31] C. Cui, W. Mao, X. Zheng, et al. Mining User Intents in Online Interactions: Applying to Discussions about Medical Event on Sina Weibo. Proceedings of ICSH, pp.177-183, 2017.

[32] 皇甫璐雯, 毛文吉. 一种基于OCC模型的文本情感挖掘方法. 智能系统学报, 12(5):645-652, 2017.

[33] Y. Zhang, W. Mao and D. Zeng. A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge. Proceedings of CIKM, pp.2017-2020, 2016.

[34] Q. Kong, W. Mao and C. Liu. Popularity Prediction Based on Interactions of Online Contents. Proceedings of CCIS, pp.1-5, 2016.

[35] L. Zhou, L. Kaati, W. Mao, et al (Eds.). Intelligence and Security Informatics. IEEE Press, 2015.

[36] Y. Zhang, W. Mao and D. Zeng. Constructing Topic Hierarchies from Social Media Data. Proceedings of ICDM Workshops (ISI-ICDM), pp.1015-1018, 2015.

[37] D. Zeng and W. Mao. Supporting Global Collective Intelligence via Artificial Intelligence. IEEE Intelligent Systems, 29(2):2-4, 2014.

[38] 毛文吉, 曾大军. 基于认知和社会心理学的行为评估与情感建模. 社会物理学: 社会治理, 第24-37页. 科学出版社, 2014.

[39] 孔庆超, 毛文吉. 基于动态演化的讨论帖流行度预测. 软件学报, 25(12):2767−2776, 2014.

[40] W. Mao and J. Gratch. Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions: Extended Abstract. Proceedings of IJCAI, pp.3166-3170, 2013.

[41] L. Huangfu, W. Mao, D. Zeng, et al. OCC Model-Based Emotion Extraction from Online Reviews. Proceedings of IEEE ISI, pp.116-121, 2013.

[42] P. Su, W. Mao and D. Zeng. An Empirical Study of Cost-Sensitive Learning in Cultural Modeling. Information Systems and e-Business Management (ISeB), 11(3):437-455, 2013.

[43] C. Yang, W. Mao, X. Zheng, et al (Eds.). Intelligent Systems for Security Informatics. Elsevier, 2013.

[44] W. Mao and J. Gratch. Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions. Journal of Artificial Intelligence Research (JAIR), 44:223-273, 2012.

[45] W. Mao, J. Gratch and X. Li. Probabilistic Plan Inference for Group Behavior Prediction. IEEE Intelligent Systems, 27(4):27-36, 2012.

[46] P. Su, W. Mao, D. Zeng, et al. Mining Actionable Behavioral Rules. Decision Support Systems, 54(1):142-152, 2012.

[47] Y. Wang, W. Mao, D. Zeng, et al. Listwise Approaches Based on Feature's Ranking Discovery. Frontiers of Computer Science, 6(6):647-659, 2012.

[48] D. Zhang, W. Mao, J. Zhan, et al. Special Issue on Social Computing and E-business. Information Systems and E-Business Management (ISeB), 10(2):161-163, 2012.

[49] X. Li, W. Mao and D. Zeng. Forecasting Complex Group Behavior via Multiple Plan Recognition. Frontiers of Computer Science, 6(1):102-110, 2012.

[50] W. Mao and F. Wang. Advances in Intelligence and Security Informatics. Academic Press, 2012.

[51] 王飞跃, 李晓晨, 毛文吉等. 社会计算的基本方法与应用. 浙江大学出版社, 2012.

[52] W. Mao, A. Ge and X. Li. From Causal Scenario to Social Causality: An Attributional Approach. IEEE Intelligent Systems, 26(6):48-57, 2011.

[53] Z. Liu, D. Yang, D. Wen, W. Zhang and W. Mao. Cyber-Physical-Social Systems for Command and Control. IEEE Intelligent Systems, 26(4):92-96, 2011.

[54] W. Mao, A. Tuzhilin and J. Gratch. Social and Economic Computing. IEEE Intelligent Systems, 26(6):19-21, 2011.

[55] 毛文吉, 曾大军, 王飞跃. 社会计算的研究现状与未来. 中国计算机学会通讯, 7(12):8-11, 2011.

[56] X. Li, W. Mao, D. Zeng, et al. Automatic Construction of Domain Theory for Attack Planning. Proceedings of IEEE ISI, pp.65-70, 2010.

[57] Q. Yang, Z. Zhou, W. Mao, et al. Social Learning. IEEE Intelligent Systems, 25(4):9-11, 2010.

[58] X. Li, W. Mao, D. Zeng, et al. Performance Evaluation of Machine Learning Methods in Cultural Modeling. Journal of Computer Science and Technology (JCST), 24(6):1010-1017, 2009.

[59] B. Martinovski and W. Mao. Emotion as an Argumentation Engine: Modeling the Role of Emotion in Negotiation. Group Decision and Negotiation (GDN), 18(3):235-259, 2009.

[60] W. Mao and J. Gratch. Modeling Social Inference in Agent Society. AI & Society, 24(1):5-11, 2009.

[61] F. Wang, N. Sun, W. Mao, et al. Special Section on International Partnership Program. Journal of Computer Science and Technology (JCST), 24(6):997-999, 2009.

[62] X. Li, W. Mao, D. Zeng, et al. Agent-Based Social Simulation and Modeling in Social Computing. Proceedings of 1st International Workshop on Social Computing (SOCO), pp.401-412, 2008.

[63] X. Li, D. Zeng, W. Mao, et al. Online Communities: A Social Computing Perspective. Proceedings of 1st International Workshop on Social Computing (SOCO), pp.355-365, 2008.

[64] 毛文吉. 多智能体交互环境下的社会推理计算模型. 模式识别与人工智能, 21(6):713-720, 2008.

[65] 毛文吉. 基于MASIM的社会推理与计算系统. 系统科学与数学, 28(11):1432-1440, 2008.

[66] F. Wang, D. Zeng, K. Carley and W. Mao. Social Computing: From Social Informatics to Social Intelligence. IEEE Intelligent Systems, 22(2):79-83, 2007.

[67] W. Mao and J. Gratch. Modeling Social Inference in Virtual Agents. Proceedings of SID, pp.81-94, 2007.

[68] J. Gratch, S. Marsella and W. Mao. Towards a Validated Model of Emotional Intelligence. Proceedings of AAAI, pp.1613-1616, 2006.

[69] W. Mao and J. Gratch. Evaluating a Computational Model of Social Causality and Responsibility. Proceedings of AAMAS, pp.985-992, 2006.

[70] J. Gratch, W. Mao and S. Marsella. Modeling Social Emotions and Social Attributions. In: R. Sun (Ed.), Cognition and Multi-Agent Interaction: Extending Cognitive Modeling to Social Simulation, pp.219-251. Cambridge University Press, 2006.

[71] B. Martinovski, W. Mao, J. Gratch, et al. Mitigation Theory: An Integrated Approach. Proceedings of CogSci, pp.1407-1412, 2005.

[72] W. Mao and J. Gratch. Social Judgment in Multiagent Interactions. Proceedings of AAMAS, pp.210-217, 2004.

[73] W. Mao and J. Gratch. A Utility-Based Approach to Intention Recognition. AAMAS Workshop on Agent Tracking: Modeling Other Agents from Observation (MOO), 2004.

[74] R. Lu and W. Mao. Automatic Generation of ITS from English Text. Proceedings of ICCE, pp.319-324, 1998.

[75] 毛文吉, 陆汝钤. 基于SELD描述语言的英文文本知识自动获取. 计算机学报, 21(suppl.):105-111, 1998.

[76] R. Lu, C. Cao, Y. Chen, W. Mao, et al. A PNLU Approach to Automatic Generation of ICAI Systems. Science China (Series A), 38(suppl.):1-11, 1995.

教学情况

2020-2022学年,中国科学院大学人工智能学院,研究生课程“多智能体系统”(首席教授)

2018-2020学年,中国科学院大学人工智能学院,研究生课程“社会智能”(首席教授)

2019-2022学年,中国科学院大学人工智能学院,研究生课程“文献阅读”(主讲教师)

2016-2017学年,中国科学院大学计算机与控制学院,研究生课程“人工智能理论与实践”(主讲教师之一)

1994-1999学年,中国科学院研究生院计算机学部,研究生课程“人工智能原理”(主讲教师)

指导学生

博士研究生:

李晓晨(07硕博-12,合作导师;百度/阿里)

葛安生(08硕博-13,合作导师;百度/腾讯)

苏鹏(08普博-11,合作导师;齐鲁学院,教授)

孔庆超(11硕博-16,中科院自动化所,副研)

张育浩(12硕博-17,京东科学家/阿里)

林俊杰(13硕博-18,腾讯/字节跳动)

陈观淡(14硕博-19,阿里达摩院)

徐楠(15直博-20,中科院自动化所,特别研助)

韦鹏辉(16硕博-21,阿里妈妈)

王帅(17直博-22,百度)

曾志雄(18硕博,在读)

肖星琳(18普博,在读)

赵嘉豪(19直博,在读)

张兴伟(19普博,在读)

张睿珂(20直博,在读)

杨瀚轩(20普博,在读)

田元(21直博,在读)

陈诺(21直博,在读)

曹艺琳(22直博,在读)

刘雪龙(22直博,在读)

硕士研究生:

王永庆(08硕-11,中科院计算所,副研)

李悦群(09硕-12,百度/阿里)

皇甫璐雯(10硕-13,San Diego State Univ, Faculty)

崔宸熙(14硕-17,FreeWheel)

孔祥飞(15硕-18,滴滴出行/美团)

孙颖(19硕-22,工行研发中心)

汤伟(20硕,在读)

邹瀚仪(21硕,在读)