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]:社会计算与大数据解析

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,在美国南加州大学创新技术研究所(ICT)任研究助理,开展面向多智能体的社会计算与社会模拟技术研究。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,获南加州大学杰出学术成就奖。2006年9月加入中国科学院自动化研究所,任副研究员。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项目及重点项目课题、国家重点研发计划项目课题、中科院及部门合作项目。2015年10月至今任中国科学院大学岗位教授。

社会任职:

担任《ACM Computing Surveys》、《IEEE Intelligent Systems》等国际期刊编委,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。先后担任ACM北京分会主席、中国人工智能学会理事、中国计算机学会大数据专家委执行委员、中国指控学会大数据科学与工程专委会常务委员、IEEE SMC学会Homeland Security专委会委员等职。

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、Neural Networks、Pattern Recognition、Patterns (Cell Press)、Science China Information Sciences、Social Science Computer Review 等

担任国际会议PC/SPC:AAAI、AAMAS、ACL、AILA、AMT、CCDM、CCF AI/Bigdata、CCIS、CogSci、ECAI、EMNLP、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)

IEEE 大数据分析国际会议(IEEE ICBDA),最佳学生论文奖(2022)

IEEE 情报安全信息学年会(IEEE ISI),最佳论文奖(2021)

美国南加州大学“杰出学术成就奖",校级(2006)

出版信息

近期主要论文:

  • S. Wang, P. Wei, Q. Kong and W. Mao. A Knowledge Enhanced Learning and Semantic Composition Model for Multi-Claim Fact Checking. Knowledge-Based Systems, accepted for publication.
  • H. Yang, Q. Kong, R. Zhang and W. Mao. Efficient Spiking Variational Graph Auto-Encoders for Unsupervised Graph Representation Learning Tasks. IEEE Intelligent Systems, accepted for publication.
  • Y. Tian, R. Zhang, N. Xu and W. Mao. Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining. Proceedings of ACL 2024.
  • M. Wang, N. Xu, J. Zhao, Y. Luo and W. Mao. PromISe: Releasing the Capabilities of LLMs with Prompt Introspective Search. Proceedings of COLING 2024.
  • Y. Tian, N. Xu and W. Mao. A Theory Guided Scaffolding Instruction Framewoek for LLM-Enabled Metaphor Reasoning. Proceedings of NAACL 2024.
  • H. Zou, H. Xu, Q. Kong, Y. Cao and W. Mao. Generating Relevant Article Comments via Variational Multi-Layer Fusion. Proceedings of IJCNN 2024.
  • N. Chen, L. Li and W. Mao. Equilibrium Strategy of Pursuit-Evasion Game in Three-Dimensional Space. IEEE/CAA Journal of Automatica Sinica (JAS), 11(2):446-458, 2024.
  • J. Zhao, W. Mao and D. Zeng. Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness. IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP), 32:1237-1247, 2024.
  • H. Yang, Q. Kong and W. Mao. A Deep Latent Space Model for Directed Graph Representation Learning. Neurocomputing, 576:127342, 2024.
  • Y. Tian, N. Xu, R. Zhang and W. Mao. Dynamic Routing Transformer Network for Multimodal Sarcasm Detection. Proceedings of ACL, pp.2468-2480, 2023.
  • R. Zhang, N. Xu, H. Yang, Y. Tian and W. Mao. Target-Oriented Relation Alignment for Cross-Lingual Stance Detection. Findings of ACL, pp.6391-6404, 2023.
  • Z. Zhao and W. Mao. Generative Adversarial Training with Perturbed Token Detection for Model Robustness. Proceedings of EMNLP, pp.13012-13025, 2023.
  • Y. Tian, N. Xu, W. Mao, et al. Modeling Conceptual Attribute Likeness and Domain Inconsistency for Metaphor Detection. Proceedings of EMNLP, pp.7736-7752, 2023.
  • R. Zhang, H. Yang and W. Mao. Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework. Proceedings of EMNLP, pp.10804-10819, 2023.
  • X. Xiao, Y. Tian, Y. Luo and W. Mao. A Cognitive Knowledge Enriched Joint Framework for Social Emotion and Cause Mining. Proceedings of KSEM, pp.396-405, 2023.
  • N. Xu, J. Wang, Y. Tian, R. Zhang and W. Mao. AnANet: Association and Alignment Network for Modeling Implicit Relevance in Cross-model Correlation Classification. IEEE Transactions on Multimedia (TMM), 25:7867-7880, 2023.
  • 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), 10(6):3241-3251, 2023.
  • X. Xiao, W. Mao, Y. Sun, et al. A Cognitive Model Enhanced Sequential Method for Social Emotion Cause Identification. Information Processing & Management (IP&M), 60(3):103305, 2023.
  • X. Zhang, X. Zheng, B. Liu, X. Wang, W. Mao, et al. Towards Human-Machine Recognition Alignment: An Adversarially Robust Multimodal Retrieval Hashing Framework. IEEE Transactions on Computational Social Systems (TCSS), 10(5):2847-2859, 2023.
  • 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.
  • S. Wang, W. Mao, P. Wei, et al. Knowledge Structure Driven Prototype Learning and Verification for Fact Checking. Knowledge-Based Systems, 238:107910, 2022.
  • 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.
  • 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.
  • 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.
  • S. Wang and W. Mao. Modeling Inter-Cliam Interactions for Verifying Multiple Claims.  Proceedings of ACM CIKM, pp.3503-3507, 2021.
  • X. Zhang, X. Zheng and W. Mao. Adversarial Perturbation Defense on Deep Neural Networks. ACM Computing Surveys (CSUR), 54(8):159, 2021.
  • 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.
  • 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.
  • 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.
  • 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.
  • Z. Zeng, N. Xu and W. Mao. Event-Driven Network for Cross-Modal Retrieval. Proceedings of ACM CIKM, pp.2297-2300, 2020.
  • X. Xiao, L. Wang, Q. Kong and W. Mao. Social Emotion Cause Extraction from Online Texts. Proceedings of IEEE ISI, pp.1-6, 2020.
  • 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.
  • P. Wei, W. Mao and G. Chen. A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection. Proceedings of AAAI, pp.7249-7256, 2019.
  • N. Xu, W. Mao and G. Chen. Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis. Proceedings of AAAI, pp.371-378, 2019.
  • P. Wei and W. Mao. Modeling Transferable Topics for Cross-Target Stance Detection. Proceedings of SIGIR, pp.1173-1176, 2019.
  • 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.
  • 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.
  • G. Chen, Q. Kong, N. Xu and W. Mao. NPP: A Neural Popularity Prediction Model for Social Media Content. Neurocomputing, 333:221-230, 2019.
  • N. Xu, W. Mao and G. Chen. A Co-Memory Network for Multimodal Sentiment Analysis. Proceedings of SIGIR, pp.929-932, 2018.
  • P. Wei, J. Lin and W. Mao. Multi-Target Stance Detection via a Dynamic Memory-Augmented Network. Proceedings of SIGIR, pp.1229-1232, 2018.
  • G. Chen, N. Xu and W. Mao. An Encoder-Memory-Decoder Framework for Sub-Event Detection in Social Media. Proceedings of ACM CIKM, pp.1575-1578, 2018.
  • 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.
  • 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.

其它主要论文:

  • J. Lin, W. Mao and D. Zeng. Personality-based Refinement for Sentiment Classification in Microblog. Knowledge-Based Systems, 132:204-214, 2017.
  • N. Xu and W. Mao. MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis. Proceedings of ACM CIKM, pp.2399-2402, 2017.
  • J. Lin, W. Mao and Y. Zhang. An Enhanced Topic Modeling Approach to Multiple Stance Identification. Proceedings of ACM CIKM, pp.2167-2170, 2017.
  • Y. Zhang, W. Mao and J. Lin. Dynamic Topic Modeling in Short Texts. Proceedings of IEEE ICBK, pp.315-319, 2017.
  • 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.
  • Y. Zhang, W. Mao and D. Zeng. A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge. Proceedings of ACM CIKM, pp.2017-2020, 2016.
  • L. Zhou, L. Kaati, W. Mao, et al (Eds.). Intelligence and Security Informatics. IEEE Press, 2015.
  • D. Zeng and W. Mao. Supporting Global Collective Intelligence via Artificial Intelligence. IEEE Intelligent Systems, 29(2):2-4, 2014.
  • W. Mao and J. Gratch. Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions: Extended Abstract. Proceedings of IJCAI, pp.3166-3170, 2013.
  • L. Huangfu, W. Mao, D. Zeng, et al. OCC Model-Based Emotion Extraction from Online Reviews. Proceedings of IEEE ISI, pp.116-121, 2013.
  • 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.
  • C. Yang, W. Mao, X. Zheng, et al (Eds.). Intelligent Systems for Security Informatics. Elsevier, 2013.
  • 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.
  • W. Mao, J. Gratch and X. Li. Probabilistic Plan Inference for Group Behavior Prediction. IEEE Intelligent Systems, 27(4):27-36, 2012.
  • P. Su, W. Mao, D. Zeng, et al. Mining Actionable Behavioral Rules. Decision Support Systems, 54(1):142-152, 2012.
  • X. Li, W. Mao and D. Zeng. Forecasting Complex Group Behavior via Multiple Plan Recognition. Frontiers of Computer Science, 6(1):102-110, 2012.
  • W. Mao and F. Wang. Advances in Intelligence and Security Informatics. Academic Press, 2012.
  • W. Mao, A. Ge and X. Li. From Causal Scenario to Social Causality: An Attributional Approach. IEEE Intelligent Systems, 26(6):48-57, 2011.
  • 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.
  • W. Mao, A. Tuzhilin and J. Gratch. Social and Economic Computing. IEEE Intelligent Systems, 26(6):19-21, 2011.
  • X. Li, W. Mao, D. Zeng, et al. Automatic Construction of Domain Theory for Attack Planning. Proceedings of IEEE ISI, pp.65-70, 2010.
  • Q. Yang, Z. Zhou, W. Mao, et al. Social Learning. IEEE Intelligent Systems, 25(4):9-11, 2010.
  • 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.
  • W. Mao and J. Gratch. Modeling Social Inference in Agent Society. AI & Society, 24(1):5-11, 2009.
  • F. Wang, N. Sun, W. Mao, et al. Special Section on International Partnership Program. Journal of Computer Science and Technology, 24(6):997-999, 2009.
  • 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.
  • 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.
  • W. Mao and J. Gratch. Modeling Social Inference in Virtual Agents. Proceedings of SID, pp.81-94, 2007.
  • J. Gratch, S. Marsella and W. Mao. Towards a Validated Model of Emotional Intelligence. Proceedings of AAAI, pp.1613-1616, 2006.
  • W. Mao and J. Gratch. Evaluating a Computational Model of Social Causality and Responsibility. Proceedings of AAMAS, pp.985-992, 2006.
  • 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.
  • W. Mao and J. Gratch. Social Causality and Responsibility: Modeling and Evaluation. Proceedings of IVA, pp.191-204, 2005.
  • B. Martinovski, W. Mao, J. Gratch and S. Marsella. Mitigation Theory: An Integrated Approach. Proceedings of CogSci, pp.1407-1412, 2005.
  • W. Mao and J. Gratch. Social Judgment in Multiagent Interactions. Proceedings of AAMAS, pp.210-217, 2004.
  • W. Mao and J. Gratch. A Utility-Based Approach to Intention Recognition. AAMAS Workshop on Agent Tracking: Modeling Other Agents from Observation (MOO), 2004.
  • W. Mao and J. Gratch. The Social Credit Assignment Problem. Proceedings of IVA, pp.39-47, 2003.
  • R. Lu and W. Mao. Automatic Generation of ITS from English Text. Proceedings of ICCE, pp.319-324, 1998.
  • R. Lu, C. Cao, Y. Chen, W. Mao, et al. A PNLU Approach to Automatic Generation of ICAI Systems. Science in China (Series A), 38(suppl.):1-11, 1995.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 毛文吉, 熊竟, 董占球. 存储与处理合一的智能系统. 计算机研究与发展, 34(2):118-123, 1997.

教学情况

2020-2024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)

2023-2024学年,中国科学院大学人工智能学院,本科生课程“多智能体系统”(主讲教师)

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)

孔庆超(11硕博-16,中国科学院自动化所)

张育浩(12硕博-17,阿里/腾讯混元)

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,中科院特别研助/中科闻歌)

韦鹏辉(16硕博-21,阿里妈妈/百度)

王帅(17直博-22,百度/Chalmers Tech Univ)

肖星琳(18普博-23,中科闻歌)

曾志雄(18硕博-23,腾讯光子)

张兴伟(19普博-22,中国科学院自动化所)

赵嘉豪(19直博-24)

杨瀚轩(20普博,在读)

张睿珂(20直博,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)

何淑怡(23普博,在读)

王民政(23直博,在读)

余昭昕(24直博)

陈坤(24直博)

硕士研究生:

王永庆(08硕-11,中国科学院计算所)

李悦群(09硕-12,百度/阿里)

皇甫璐雯(10硕-13,San Diego State Univ)

崔宸熙(14硕-17,FreeWheel)

孔祥飞(15硕-18,滴滴出行/美团)

孙颖(19硕-22,工行北京研发中心)

汤伟(20硕-23,国家某部)

邹瀚仪(21硕-24,新浪微博)