姓名：张静
职称：教授
电话：
办公室：李文正楼北435
个人主页：
邮箱：jingz@seu.edu.cn
教育背景
学术兼职
IEEE高级会员（Senior Member，Since 2019）
- 常年担任AAAI、IJCAI、KDD、ICML、NeurIPS、ICLR、ICDM、WSDM、SDM、CIKM、ECAI等人工智能相关领域顶级和权威国际会议程序委员会成员（PC/SPC Member）
研究领域
安全可信人工智能
数据挖掘与机器学习
大数据与内容安全
研究概况
主要研究方向为安全可信人工智能及应用，包括但不限于：
可信人工智能（噪声鲁棒性学习、不平衡学习、小样本学习等特定场景下的学习方法）
人工智能安全（样本对抗攻防、数据投毒攻防、后门攻击与防御）
人在回路（Human-In-the-Loop）机器学习（众包学习、主动学习、可靠激励机制）
联邦学习与图表示学习及其安全机制
隐私保护的学习（联邦学习、迁移学习、知识蒸馏）
大模型与内容安全（大模型精调、提示工程、知识图谱与大模型融合、AIGC内容检测）
应用领域：可信AI+（医疗、金融、网络安全、软件工程、工业互联网等）
最近动态
研究课题
2024 企业横向委托课题，主持，在研（2024.01-2024.12）
2022 中央高校基本科研业务费（东南大学科研启动经费），主持，在研（2023.01-2025.12）
2021 之江实验室开放课题项目，2019KD0AD01/015，交互式众包标注主动学习方法研究，主持，已结题（2022.02-2023.02）
2020 国家自然科学基金，面上项目，62076130，面向众包标注数据的机器学习方法研究，主持，已结题（2021.01-2024.12）
2019 国家重点研发计划，科技创新2030-“新一代人工智能”重大项目，2018AAA0102002，分布式跨媒体知识获取与管理，子课题负责人，已结题（2019.12-2023.12）
2018 国家自然科学基金，重大研究计划（大数据驱动的管理与决策研究）培育项目，91846104，众包大数据多源异构融合与知识学习，主持，已结题（2019.01-2021.12）
2016 国家自然科学基金，青年科学基金，61603186，面向众包标注的真值推断与监督分类关键问题研究，主持，已结题（2017.01-2019.12）
2017 中国博士后科学基金，特别资助项目，2017T100370，融合样本特征的众包标注数据学习方法研究，主持，已结题（2017.01-2018.12）
2016 中国博士后科学基金，面上项目（一等），2016M590457，面向图像分类众包标注的监督学习方法研究，主持，已结题（2016.01-2017.12）
2016 苏省自然科学基金青年项目，BK20160843，面向众包标注的主动学习方法研究，主持，已结题（2016.07-2019.06）
2016 江苏省博士基金C类资助，1601199C，主持，已结题（2016.01-2017.12）
2016 江苏省社会公共安全图像与视频理解重点实验室开放课题，30916014107，主持，已结题（2016.07-2018.06）
2015 中央高校基本科研业务费，主持，已结题（2015.07-2017.06）
奖励与荣誉
2024年度 江苏省通信学会 科学技术奖 三等奖 排一
2024年度 第17届全国大学生信息安全竞赛 作品赛 全国三等奖 导师
2024年度 中国研究生创“芯”大赛·EDA精英挑战赛 全国三等奖 导师
2020年度 IEEE ICDM-2020（国际数据挖掘会议）学生旅行奖 导师
2017年度 Information Fusion期刊优秀审稿人奖
课程信息
《数据结构基础》（本科生64课时）
《人工智能安全》（研究生32课时）
学术成果
发表各类期刊和会议论文100余篇，授权发明专利4项，登记软件著作权3项。
【代表性期刊论文,*通讯作者】
[18] Xiaoqian Jiang, Jing Zhang*, Ming Wu, & Cangqi Zhou. (2024). TiFedCrowd: Federated Crowdsourcing with Time-Controlled Incentive. IEEE Transactions on Emerging Topics in Computational Intelligence.
[17] Jing Zhang, Ming Wu, Zeyi Sun, & Cangqi Zhou. (2024). Learning from crowds using graph neural networks with attention mechanism. IEEE Transactions on Big Data.
[16] Cangqi Zhou, Hui Chen, Jing Zhang*, Qianmu Li, & Dianming Hu. (2024). Quintuple-based representation learning for bipartite heterogeneous networks. ACM Transactions on Intelligent Systems and Technology.
[15] Zijian Ying, Jing Zhang*, Qianmu Li, Ming Wu, & Victor S. Sheng. (2023). A little truth injection but a big reward: Label aggregation with graph neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Jing Zhang, Yu Lei, Yuxiang Wang, Cangqi Zhou, & Victor S. Sheng. (2023). Hierarchical graph capsule networks for molecular function classification with disentangled representations. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[13] Jing Zhang, Ming Wu, Cangqi Zhou, & Victor S. Sheng. (Mar. 2024). Active Crowdsourcing for Multilabel Annotation. IEEE Transactions on Neural Networks and Learning Systems, 35(3): 3549–3559.
[12] Jing Zhang, Sunyue Xu, & Victor S. Sheng. (Aug. 2023). CrowdMeta: Crowdsourcing truth inference with meta-knowledge transfer. Pattern Recognition, 140: 109525
[11] Jing Zhang. (2022). Knowledge learning with crowdsourcing: A brief review and systematic perspective. IEEE/CAA Journal of Automatica Sinica, 9(5): 749–762.
[10] Jing Zhang & Xindong Wu. (2021). Multi-label truth inference for crowdsourcing using mixture models. IEEE Transactions on Knowledge and Data Engineering. 33(5): 2083–2095.
[ 9 ] Jing Zhang, Mengxi Li, Kaisheng Gao, Shunmei Meng, & Cangqi Zhou. (2021). Word and graph attention networks for semi-supervised classification. Knowledge and Information Systems, 63: 2841–2859.
[ 8 ] Jing Zhang, Victor S. Sheng, & Jian Wu. (2019). Crowdsourced label aggregation using bilayer collaborative clustering. IEEE Transactions on Neural Networks and Learning Systems, 30(10): 3172–3185.
[ 7 ] Jing Zhang, Ming Wu, & Victor S. Sheng (2019). Ensemble learning from crowds. IEEE Transactions on Knowledge and Data Engineering, 31(8): 1506–1519.
[ 6 ] Victor S. Sheng, Jing Zhang*, Bin Gu*, & Xindong Wu. (2019). Majority voting and pairing with multiple noisy labeling. IEEE Transactions on Knowledge and Data Engineering, 31(7): 1355–1368.
[ 5 ] Jing Zhang, Victor S. Sheng, Tao Li, & Xindong Wu. (2018). Improving crowdsourced label quality using noise correction. IEEE Transactions on Neural Networks and Learning Systems, 29(5): 1675–1688.
[ 4 ] Jing Zhang, Victor S. Sheng, Jian Wu, & Xindong Wu. (2016). Multi-class ground truth inference in crowdsourcing with clustering. IEEE Transactions on Knowledge and Data Engineering, 28(4): 1080–1085.
[ 3 ] Jing Zhang, Xindong Wu, & Victor S. Sheng. (2015). Active learning with imbalanced multiple noisy labeling. IEEE Transactions on Cybernetics, 45(5): 1081–1093.
[ 2 ] Jing Zhang, Xindong Wu, & Victor S. Sheng. (2015). Imbalanced multiple noisy labeling. IEEE Transactions on Knowledge and Data Engineering, 27(2): 489–503.
[ 1 ] Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, & Xindong Wu. (2015). CEKA: A tool for mining the wisdom of crowds. Journal of Machine Learning Research, 16: 2853–2858.
【代表性会议论文,*通讯作者】
[14] Maochang Zhao & Jing Zhang*. (2025). Highly Imperceptible Black-Box Graph Injection Attacks with Reinforcement Learning. In the 39th AAAI Conference on Artificial Intelligence (AAAI-2025), Philadelphia, Pennsylvania, USA.
[13] Xiaoqian Jiang, Haiyang Diao, Cangqi Zhou, & Jing Zhang*. (2024). Timeliness-Selective Incentive Federated Crowdsourcing. In 2024 IEEE International Conference on Web Service (ICWS-2024) , Shenzhen, China.
[12] Cangqi Zhou, Yuxiang Wang, Jing Zhang*, Jiqiong Jiang, & Dianming Hu. (2022). End-to-end modularity-based community co-partition in bipartite networks. In the 31th ACM International Conference on Information and Knowledge Management (CIKM-2022), Atlanta, GA, USA.
[11] Cangqi Zhou, Hui Chen, Jing Zhang*, Qianmu Li, & Dianming Hu. (2022). AngHNE: Representation learning for bipartite heterogeneous networks with angular loss. In the 15th International Conference on Web Search and Data Mining (WSDM-2022), Phoenix, Arizona, USA.
[10] Cangqi Zhou, Jinling Shang, Jing Zhang*, Qianmu Li, & Dianming Hu. (2021). Topic-attentive encoder-decoder with pre-trained language model for keyphrase generation. In the 21st International Conference on Data Mining (ICDM-2021), Auckland, New Zealand.
[ 9 ] Yu Lei & Jing Zhang*. (2021). Capsule graph neural networks with EM routing. In the 30th ACM International Conference on Information and Knowledge Management (CIKM-2021), Gold Coast, Queensland, Australia.
[ 8 ] Jing Zhang, Huihui Wang, Shunmei Meng, & Victor S. Sheng. (2020). Interactive learning with proactive cognitive enhancement for crowd workers. In the 34th AAAI Conference on Artificial Intelligence (AAAI-2020), New York, USA.
[ 7 ] Yanhui Peng & Jing Zhang*. (2020). LineaRE: Simple but powerful knowledge graph embedding for link prediction. In the 20th IEEE International Conference on Data Mining (ICDM-2020), Sorrento, Italy.
[ 6 ] Victor S. Sheng & Jing Zhang*. (2019). Machine learning with crowdsourcing: A brief summary of the past research and future directions. In the 33rd AAAI Conference on Artificial Intelligence (AAAI-2019), Honolulu, Hawaii, USA.
[ 5 ] Huihui Wang, Shunmei Meng, Jinbiao Yu, & Jing Zhang*. (2019). Fast classification algorithms via distributed accelerated alternating direction method of multipliers. In the 2019 International Conference on Data Mining (ICDM-2019), Beijing, China.
[ 4 ] Jing Zhang & Xindong Wu. (2018). Multi-label inference for crowdsourcing. In the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2018), London, UK.
[ 3 ] Jing Zhang, Victor S. Sheng, & Tao Li. (2017). Label aggregation for crowdsourcing with bi-layer clustering. In the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2017), Tokyo, Japan.
[ 2 ] Jing Zhang, Victor S. Sheng, Jian Wu, Xiaoqin Fu, & Xindong Wu. (2015). Improving label quality in crowdsourcing using noise correction. In the 24th ACM International Conference on Information and Knowledge Management (CIKM-2015), Melbourne, Australia.
[ 1 ] Jing Zhang, Xindong Wu, & Victor S. Sheng. (2013). Imbalanced multiple noisy labeling for supervised learning. In the 27th AAAI Conference on Artificial Intelligence (AAAI-2013), Bellevue, Washington, USA.
【授权发明专利】
[ 3 ] 第一发明人. 基于标注者可靠度时序建模的众包主动学习方法和装置. 202210512110.0
[ 2 ] 第一发明人. 医疗咨询信息聚合分析方法. 201811211126.8.
[ 1 ] 第一发明人. 一种分布式MRCP服务器负载均衡系统的均衡方法. 200910185900.7.
