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）

- ACM会员、CCF协同计算专委会执行委员

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

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（噪声鲁棒学习、不平衡学习、小样本学习、模型可解释性、公平性等）

• 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、成员与属性推断）

• 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）

• 大模型安全（大模型微调、检索增强、大模型投毒、越狱攻击与防御、多Agent攻击与防御）
  

• 数据和内容安全（数据治理、AIGC内容检测）

• 应用领域：可信AI+（网络安全、软件工程、工业互联网、医疗、金融等）

2025 江苏省自然科学基金面上项目，人在回路的工业互联网多源异构数据融合与知识学习，BK20251747，主持，在研（2025.07-2028.06）

2025 某国家级科研项目，*** ***，主持，在研（2025.07-2026.06）

2025 企业委托项目-新型电力系统的物联网终端接入检测研究，主持，在研（2025.02-2027.12）

2024 企业委托项目-后量子密码学研究，主持，在研（2024.01-2025.12）

2023 中央高校基本科研业务费（东南大学科研启动经费），众包机器学习中的安全问题研究， RF1028623059，主持，在研（2023.03-2026.02）

2022 【教学研究】东南大学教学改革研究与实践项目，面向网络空间安全专业的《数据结构基础》课程资源建设探索与实践，2023-152，主持，已结题（2023.01-2014.12）

2021 之江实验室开放课题项目，交互式众包标注主动学习方法研究，2019KD0AD01/015，主持，已结题（2022.02-2023.02）

2020 国家自然科学基金，面上项目，面向众包标注数据的机器学习方法研究， 62076130，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）

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 国家自然科学基金，青年科学基金项目（No. 61603186），主持，已结题（2017.01-2019.12）

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 江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（科研启动经费），主持，已结题（2015.07-2017.06）

2025年度 第一届全国大学生人工智能安全竞赛 全国一等奖 导师

2024年度 东南大学优秀本科生毕业论文 导师

2024年度 江苏省通信学会 科学技术奖 三等奖 排一

2024年度 中国电力发展促进会 科学技术奖 二等奖 排五

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

2023年度 东南大学优秀班主任

2023年度 东南大学青年教师授课竞赛 提名奖

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

“数据结构基础”（本科生64课时）主讲

“人工智能安全”（研究生32课时）主讲

辅讲“人工智能”、“网络空间安全前沿”等本科和研究生研讨课程

发表各类期刊和会议论文110余篇，授权发明专利4项，登记软件著作权4项。

【代表性期刊论文,*通讯作者】  

[20] Shicheng Cui, Deqiang Li & Jing Zhang*. (Online 2025). MC-GNN: Multi-channel graph neural networks with Hilbert-Schmidt independence criterion. IEEE Transactions on Big Data.

[19] Shicheng Cui, Deqiang Li & Jing Zhang*. (2025). Dynamic multi-scale feature augmentation for inductive network representation learning. Pattern Recognition, 161: 111250.

[18] Xiaoqian Jiang, Jing Zhang*, Ming Wu, & Cangqi Zhou. (2025). TiFedCrowd: Federated crowdsourcing with time-controlled incentive.IEEE Transactions on Emerging Topics in Computational Intelligence, 9(2): 1514–1526.

[17] Jing Zhang, Ming Wu, Zeyi Sun, & Cangqi Zhou. (2025). Learning from crowds using graph neural networks with attention mechanism. IEEE Transactions on Big Data, 11(1): 86–98.

[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(3): 61.

[15] Zijian Ying, Jing Zhang*, Qianmu Li, Ming Wu, & Victor S. Sheng. (2024). A little truth injection but a big reward: Label aggregation with graph neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(5): 3169–3182.

[14] Jing Zhang, Yu Lei, Yuxiang Wang, Cangqi Zhou, & Victor S. Sheng. (2024). Hierarchical graph capsule networks for molecular function classification with disentangled representations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(4): 1072–1082.

[13] Jing Zhang, Ming Wu, Cangqi Zhou, & Victor S. Sheng. (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. (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】

[15] Xiaoqian Jiang & Jing Zhang*. (2025). FedClean: A general robust label noise correction for federated learning. In the 42nd International Conference on Machine Learning (ICML-2025), Vancouver, Canada.

[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 the 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.