Brief Biography

I am currently a Full Professor of Computer Science in the School of Cyber Science and Engineering at Southeast University (SEU). I obtained my Ph.D. degree in Computer Science from Hefei University of Technology, Hefei, China, in 2015. During my Ph.D. study, I worked as a Visiting Student in the Department of Computer Science at the University of Central Arkansas under the support of the China Scholarship Council from September 2013 through September 2014. After obtaining my Ph.D. degree, I became an Assistant Professor in the School of Computer Science and Engineering at Nanjing University of Science and Technology (NJUST), in June 2015. I was promoted to Associate Professor in July 2017. I worked as a Visiting Scholar in the Faculty of Engineering and Information Technology at the University of Technology, Sydney from July 2017 through September 2017 and a Visiting Research Scholar in the Department of Electrical and Computer Engineering at the University of Pittsburgh from March 2019 through March 2020. I was promoted to Senior Member of IEEE in 2019. I became an Associate Professor at Southeast University in June 2022 and was promoted to Full Professor in April 2023.

Research Interests

Secure and Trustworthy Artifical Intelligence

Data Mining and Machine Learning

Human-in-the-Loop Computation

AI+ Cyber Security, Finance, Madicine, ...

Teaching

Undergraduate

Data Structure Basic《数据结构基础》(Fall 2022, Fall 2023, Fall 2024, Fall 2025)

Information Hiding Technology 《信息隐藏技术》(Fall 2020)

Intelligent Analysis and Decision-Making for Complex Engineering Problems 《复杂工程问题中的智能决策》(Fall 2020)

Compiling Methodology (with Course Project) 《编译方法（含课程设计）》(Spring 2022, Spring 2021, Spring 2020, Spring 2018, Spring 2017)

Software Architecture 《软件体系结构》(Spring 2018)

Software Project Management 《软件项目管理》(Fall 2016)

An Introduction to Computational Thinking 《计算思维导论》(Fall 2015)

Graduate

AI Security 《人工智能安全》(Fall 2024, Fall 2025)

Information Security Technology 《信息安全技术》(Fall 2021, Fall 2020)

Software Modeling Training 《软件建模训练》(Spring 2016)

Funding & Awards

2024 Jiangsu Institute of Communications, Science and Technology Award, Third Prize (First)

2024 China Electric Power Promotion Council, Science and Technology Award, Second Prize (Fifth)

2024 Enterprise Entrusted Project (No. 8509016158, 2024.1–2025.12, PI)

2021 Open Research Projects of Zhejiang Lab (No. 2019KD0AD01/015, 2022.2–2023.1, PI)

2020 National Natural Science Foundation of China (NSFC) (General Project) (No. 62076130, 2021.1–2024.12, PI)

2018 National Natural Science Foundation of China (NSFC) (Fostering Project of the Major Research Plan) (No. 91846104, 2019.1–2021.12, PI)

2018 The Sponsorship of Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-Aged Teachers and Presidents for visiting at the University of Pittsburgh (2019.3–2020.2)

2017 Information Fusion (ELSEVIER) Outstanding Reviewer Award.

2016 National Natural Science Foundation of China (NSFC) (Youth) (No. 61603186, 2017.1–2019.12, PI)

2015 The Start-up Funding of Nanjing University of Science and Technology (2015.7–2017.6, PI)

2014 National Ministry of Education, China National Scholarship for Ph. D. Students

2013 China Scholarship Council (CSC) scholarship for visiting at the University of Central Arkansas (2013.9–2014.9)

Publications [111]

Note: The CCF rankings are periodically provided by the China Computer Federation. Symbol * represents that I am one of the corresponding authors when I am not the first author.

Journals (63)

[J063] Shicheng Cui, Deqiang Li & Jing Zhang*. (Online 2024). MC-GNN: Multi-channel graph neural networks with Hilbert-Schmidt independence criterion. IEEE Transactions on Big Data. (CCF C).

[J062] Shicheng Cui, Deqiang Li & Jing Zhang*. (May 2025). Dynamic multi-scale feature augmentation for inductive network representation learning. Pattern Recognition, 161: 111250. (CCF B)

[J061] Xiaoqian Jiang, Jing Zhang*, Ming Wu, & Cangqi Zhou. (Apr. 2025). TiFedCrowd: Federated crowdsourcing with time-controlled incentive. IEEE Transactions on Emerging Topics in Computational Intelligence, 9(2): 1514–1526.

[J060] Wan Zhang & Jing Zhang*. (Mar. 2025). Hallucination mitigation for retrieval-augmented large language models: A review. Mathematics, 13(5): 856.

[J059] Jing Zhang, Ming Wu, Zeyi Sun, & Cangqi Zhou. (Feb. 2025). Learning from crowds using graph neural networks with attention mechanism. IEEE Transactions on Big Data, 11(1): 86–98. (CCF C)

[J058] Jing Zhang, Yu Lei, Yuxiang Wang, Cangqi Zhou, & Victor S. Sheng. (Aug. 2024). Hierarchical graph capsule networks for molecular function classification with disentangled representations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(4): 1072–1082. (CCF B)

[J057] Sunyue Xu, Jing Zhang*, Shunmei Meng, & Jian Xu. (July 2024). Task allocation for unmanned aerial vehicles in mobile crowdsensing. Wireless Networks, 30: 3707–3719. (CCF C)

[J056] Huihui Wang, Mingfei Xiao, Changsheng Wu, & Jing Zhang. (July 2024). Distributed classification for imbalanced big data in distributed environments. Wireless Networks, 30: 3657–3668. (CCF C)

[J055] Shiyu Chen, Jun Hou, Qianmu Li, Shunmei Meng, & Jing Zhang. (July 2024). Temporal-aware influence maximization solution in artificial intelligent edge application. Wireless Networks, 30: 4301–4313. (CCF C)

[J054] Jing Zhang, Xiaoqian Jiang, Nianshang Tian, & Ming Wu. (July 2024). Label noise correction for crowdsourcing using dynamic resampling. Engineering Applications of Artificial Intelligence, 133(Part D): 108439 (11 pages). (CCF C)

[J053] Cangqi Zhou, Hui Chen, Jing Zhang*, Qianmu Li, & Dianming Hu. (May 2024). Quintuple-based representation learning for bipartite heterogeneous networks. ACM Transactions on Intelligent Systems and Technology, 15(3): 61.

[J052] Zijian Ying, Jing Zhang*, Qianmu Li, Ming Wu, & Victor S. Sheng. (May 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. (CCF A)

[J051] 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. (CCF B)

[J050] Jing Zhang, Jipeng Qiang, & Cangqi Zhou. (Jan. 2024). Editorial: New horizons in web search, web data mining, and web-based applications. Applied Sciences - Basel, 14(2): 530 (5 pages).

[J049] Jing Zhang, Jianhua Li, Yi Zhu, Yu Fu, & Lixia Chen. (Oct. 2023). ThyroidKeeper: A healthcare management system for patients with thyroid diseases. Health Information Science and Systems, 11: 49 (20 pages). (2023 IF=4.7)

[J048] Jiabo Zhuang, Shunmei Meng*, Jing Zhang, & Victor S. Sheng. (Sep. 2023). Contrastive learning based graph convolution network for social recommendation. ACM Transactions on Knowledge Discovery from Data, 17(8): 120. (2023 IF=4.0, CCF B)

[J047] Jing Zhang, Sunyue Xu, & Victor S. Sheng. (Aug. 2023). CrowdMeta: Crowdsourcing truth inference with meta-knowledge transfer. Pattern Recognition, 140: 109525. (2023 IF=7.5, CCF B)

[J046] Jing Zhang & Meilin Cao. (Jun. 2023). Distant supervision for relation extraction with hierarchical attention-based networks. Expert Systems with Applications, 220: 119727. (2023 IF=7.5, CCF C)

[J045] Ming Wu, Qianmu Li *, Fei Yang, Jing Zhang*, Victor S. Sheng, & Jun Hou. (Jan. 2023). Learning from biased crowdsourced labeling with deep clustering. Expert Systems with Applications, 211: 118608. (2023 IF=7.5, CCF C)

[J044] Shunmei Meng, Jiangmin Xu, Huihui Wang, Rui Yuan, Jing Zhang, & Qianmu Li. (Sep. 2022). Time-aware scalable recommendation with clustering-based distributed factorization for edge services. World Wide Web Journal, 25: 1831–1849. (2022 IF=3.7, CCF B)

[J043] Wen Hao, Jing Zhang, Jun Su, YuqingSong, Zhe Liu*, Yi Liu, Chengjian Qiu, & Kai Han. (Sep. 2022). HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images. Computer Methods and Programs in Biomedicine, 224: 107003. (2022 IF=6.1)

[J042] Qingren Wang, Jian Lu, Wei Li, Jing Zhang*, & Victor S. Sheng. (Aug. 2022). Homophily-aware correction framework for crowdsourced labels using heterogeneous information network. Expert Systems with Applications, 200: 116896. (2022 IF=8.5, CCF C)

[J041] Shunmei Meng, Shaoyu Fan, Qianmu Li, Xinna Wang, & Jing Zhang, Xiaolong Xu, Lianyong Qi, Md Zakirul Alam Bhuiyan. (Aug. 2022). Privacy-aware factorization-based hybrid recommendation method for healthcare services. IEEE Transactions on Industrial Informatics, 18(8): 5637–5647. (2022 IF=12.3, CCF C)

[J040] Cangqi Zhou, Jing Zhang*, Kaisheng Gao, Qianmu Li, Dianming Hu, & Victor S. Sheng. (Jul. 2022). Bipartite network embedding with symmetric neighborhood convolution. Expert Systems with Applications, 198: 116757. (2022 IF=8.5, CCF C)

[J039] Yanhui Peng, Jing Zhang*, Cangqi Zhou, & Shunmei Meng. (Jul. 2022). Knowledge graph entity alignment using relation structural similarity. Journal of Database Management, 33(1): 19 pages. (2022 IF=2.6, CCF C)

[J038] Jing Zhang*. (May 2022). Knowledge learning with crowdsourcing: A brief review and systematic perspective. IEEE/CAA Journal of Automatica Sinica, 9(5): 749–762. (2022 IF=11.8, CCF T1)

[J037] Milad Taleby Ahvanooey, Xuefang Zhu, Wojciech Mazurczyk, Kim-Kwang Raymond Choo, Mauro Conti, & Jing Zhang. (Jan. 2022). Misinformation detection on social media: Challenges and the road ahead. IEEE IT Professional Magazine, 24(1): 34–40. (2022 IF=2.6)

[J036] Jing Zhang*, Mengxi Li, Kaisheng Gao, Shunmei Meng, & Cangqi Zhou*. (Nov. 2021). Word and graph attention networks for semi-supervised classification. Knowledge and Information Systems, 63: 2841–2859. (2021 IF=2.531, CCF B)

[J035] Jun Su, Zhe Liu, Jing Zhang, Victor S. Sheng, Yuqing Song, Yan Zhu & Yi Liu. (Nov. 2021). DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function. Knowledge-Based Systems, 232: 107471. (2021 IF=8.139, CCF C)

[J034] Ming Wu, Qianmu Li, Muhammad Bilal*, Xiaolong Xu, Jing Zhang, & Jun Hou. (Oct. 2021). Multi-label active learning from crowds for secure IIoT. Ad Hoc Networks, 121: 102594. (2021 IF=4.816, CCF C)

[J033] Cangqi Zhou, Hui Chen, Jing Zhang*, Qianmu Li, Dianming Hu & Victor S. Sheng. (Oct. 2021). Multi-label graph node classification with label attentive neighborhood convolution. Expert Systems with Applications, 180: 115063. (2021 IF=8.665, CCF C)

[J032] Jing Zhang & Xindong Wu. (May 2021). Multi-label truth inference for crowdsourcing using mixture models. IEEE Transactions on Knowledge and Data Engineering, 33(5): 2083–2095. (2021 IF=9.235, CCF A)

[J031] Zhe Liu, Kai Han, Zhaohui Wang, Jing Zhang, Yuqing Song, Xu Yao, Deqi Yuan, & Victor S. Sheng. (Feb. 2021). Automatic liver segmentation from abdominal CT volumes using improved convolution neural networks. Multimedia Systems, 27: 111–124. (2021 IF=2.603, CCF C)

[J030] Cangqi Zhou, Hao Ban*, Jing Zhang, Qianmu Li, & Yinghua Zhang*. (Jun. 2020). Gaussian mixture variational autoencoder for semi-supervised topic modeling. IEEE Access, 8: 106843–106854. (2020 IF=3.367)

[J029] Qianmu Li, Yanjun Song, Jing Zhang*, & Victor S. Sheng. (Jun. 2020). Multiclass imbalanced learning with one-versus-one decomposition and spectral clustering. Expert Systems with Applications, 147: 1–14. (2020 IF=6.954, CCF C)

[J028] Ming Wu, Xiaochun Yin, Qianmu Li*, Jing Zhang, Xinqi Feng, Qi Cao, & Haiyuan Shen. (Apr. 2020). Learning deep networks with crowdsourcing for relevance evaluation. EURASIP Journal on Wireless Communications and Networking, 82: 1–11. (2020 IF=1.408)

[J027] Jianhan Pan, Teng Cui, Thuc Duy Le, Xiaomei Li, & Jing Zhang. (Mar. 2020). Multi-group transfer learning on multiple latent spaces for text classification. IEEE Access, 8: 64120–64130. (2020 IF=3.367)

[J026] Jian Wu, Victor S. Sheng*, Jing Zhang, Hua Li, Tetiana Dadakova, Christine Swisher, Zhiming Cui, & Pengpeng Zhao*. (Mar. 2020). Multi-label active learning algorithms for image classification: Overview and future promise. ACM Computing Surveys, 53(2): 28. (2020 IF=7.990)

[J025] Milad Taleby Ahvanooey, Qianmu Li, Xuefang Zhu, Mamoun Alazab, & Jing Zhang. (Mar. 2020). ANiTW: A novel intelligent text watermarking technique for forensic identification of spurious information on social media. Computers & Security, 90: 101702. (2020 IF=4.438, CCF B)

[J024] Jing Zhang, Jianhan Pan, Zhicheng Cai, Min Li, & Lin Cui. (Jan. 2020). Knowledge transfer using user-generated data within real-time cloud services. KSII Transactions on Internet and Information Systems, 14(1): 77–92. (2020 IF=0.858)

[J023] Shunmei Meng, Qianmu Li, Jing Zhang, Wenmin Lin, & Wanchun Dou*. (Jan. 2020). Temporal-aware and sparsity-tolerant hybrid collaborative recommendation method with privacy preservation. Concurrency and Computation: Practice and Experience, 32(2): e5447. (2020 IF=1.536, CCF C)

[J022] Jing Zhang, Victor S. Sheng, & Jian Wu. (Oct. 2019). Crowdsourced label aggregation using bilayer collaborative clustering. IEEE Transactions on Neural Networks and Learning Systems, 30(10): 3172–3185. (2019 IF=8.793, CCF B)

[J021] Shunmei Meng, Qianmu Li, Taoran Wu, Weijia Huang, Jing Zhang, & Weimin Li. (Aug. 2019). A fault-tolerant dynamic scheduling method on hierarchical mobile edge cloud computing. Computational Intelligence, 35(3): 577–598. (2019 IF=1.196, CCF C)

[J020] Jing Zhang, Ming Wu, & Victor S. Sheng (Aug. 2019). Ensemble learning from crowds. IEEE Transactions on Knowledge and Data Engineering, 31(8): 1506–1519. (2019 IF=4.935, CCF A)

[J019] Victor S. Sheng, Jing Zhang*, Bin Gu*, & Xindong Wu. (Jul. 2019). Majority voting and pairing with multiple noisy labeling. IEEE Transactions on Knowledge and Data Engineering, 31(7): 1355–1368. (2019 IF=4.935, CCF A)

[J018] Qianmu Li, Shunmei Meng, Shuo Wang, Jing Zhang, & Jun Hou. (Apr. 2019). CAD: Command-level anomaly detection for vehicle-road collaborative charging network. IEEE Access, 7: 34910–34924. (2019 IF=3.745)

[J017] Qianmu Li, Shunmei Meng, Sainan Zhang, Ming Wu, Jing Zhang, Milad Taleby Ahvanooey, & Muhammad Shamrooz Aslam. (Jan. 2019). Safety risk monitoring of cyber-physical power systems based on ensemble learning algorithm. IEEE Access, 7: 24788–24805. (2019 IF=3.745)

[J016] Milad Taleby Ahvanooey, Qianmu Li, Jun Hou, Hassan Dana Mazraeh, & Jing Zhang. (Aug. 2018). AITSteg: An innovative text steganography technique for hidden transmission of text message via social media. IEEE Access, 6: 65981–65995. (2018 IF=4.098)

[J015] Jing Zhang, Victor S. Sheng, Tao Li, & Xindong Wu. (May 2018). Improving crowdsourced label quality using noise correction. IEEE Transactions on Neural Networks and Learning Systems, 29(5): 1675–1688. (2018 IF=11.683, CCF B)

[J014] Jing Zhang, Shicheng Cui, Yan Xu, Qianmu Li, & Tao Li. (May 2018). A novel data-driven stock price trend prediction system. Expert Systems with Applications, 97: 60–69. (2018 IF = 4.292, CCF C)

[J013] Jian Wu, Chen Ye, Victor S. Sheng, Jing Zhang, Pengpeng Zhao, & Zhiming Cui. (Oct. 2017). Active learning with label correlation exploration for multi-label image classification. IET Computer Vision, 11(7): 577–584. (2017 IF=1.087, CCF C)

[J012] Jian Wu, Shiquan Zhao, Victor S. Sheng, Jing Zhang, Chen Ye, Pengpeng Zhao, & Zhiming Cui. (Jun. 2017). Weak labeled active learning with conditional label dependence for multi-label image classification. IEEE Transactions on Multimedia, 19(6): 1156–1169. (2017 IF=3.977, CCF B)

[J011] Lin Cui, Dechang Pi, & Jing Zhang.(May 2017). DMFA-SR: Deeper membership and friendship awareness for social recommendation. IEEE Access, 5: 8904–8915. (2017 IF=3.557)

[J010] Jing Zhang, Victor S. Sheng, Qianmu Li, Jian Wu, & Xindong Wu. (Mar. 2017). Consensus algorithms for biased labeling in crowdsourcing. Information Sciences, 382: 254–273. (2017 IF=4.305, CCF B)

[J009] Bryce Nicholson, Victor S. Sheng, & Jing Zhang. (Dec. 2016). Label Noise Correction and application in crowdsourcing. Expert Systems with Applications, 66: 149–162. (2016 IF=3.928, CCF C)

[J008] Jing Zhang, Xindong Wu, & Victor S. Sheng. (Dec. 2016). Learning from crowdsourced labeled data: A survey. Artificial Intelligence Review, 46(4): 543–576. (2016 IF=2.627)

[J007] Jing Zhang, Qianmu Li, & Wei Zhou. (Sep. 2016). HDCache: A distributed cache system for real-time cloud services. Journal of Grid Computing. 14(3): 407–428. (2016 IF=2.766, CCF C)

[J006] Jing Zhang, Victor S. Sheng, Jian Wu, & Xindong Wu. (Apr. 2016). Multi-class ground truth inference in crowdsourcing with clustering. IEEE Transactions on Knowledge and Data Engineering, 28(4): 1080–1085. (2016 IF=3.438, CCF A)

[J005] Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, & Xindong Wu. (Dec. 2015). CEKA: A tool for mining the wisdom of crowds. Journal of Machine Learning Research, 16: 2853–2858. (2015 IF=2.450, CCF A)

[J004] Jing Zhang, Xindong Wu, & Victor S. Sheng. (May 2015). Active learning with imbalanced multiple noisy labeling. IEEE Transactions on Cybernetics, 45(5): 1081–1093. (2015 IF=4.943, CCF B)

[J003] Jing Zhang, Xindong Wu, & Victor S. Sheng. (Feb. 2015). Imbalanced multiple noisy labeling. IEEE Transactions on Knowledge and Data Engineering, 27(2): 489–503. (2015 IF=2.476, CCF A)

[J002] Haiyan Wang, Lei Zheng, Xueping Xu, & Jing Zhang*. (Sep. 2014). MLE ground truth inference and its application in teaching evaluation. Journal of Anhui University (Natural Science Edition), 38(5): 16–23. (In Chinese)

[J001] Jing Zhang & Weimin Lei. (Oct. 2006). Design and implementation of a media-supported SIP performance test tool. Chinese Mini-Micro Systems, 27(10): 1831–1836. (In Chinese, INSPEC)

Conferences (46)

[C046] Maochang Zhao & Jing Zhang*. (Feb. 25-Mar. 4, 2025). Highly imperceptible black-box graph injection attacks with reinforcement learning. In The 39th Annual AAAI Conference on Artificial Intelligence (AAAI-2024), Philadelphia, Pennsylvania, USA. (CCF A, EI)

[C045] Xiaoqian Jiang, Haiyang Diao, Cangqi Zhou, & Jing Zhang*. (July 7-13, 2024). Timeliness-selective incentive federated crowdsourcing. In 2024 IEEE International Conference on Web Service (ICWS-2024) , Shenzhen, China, pp. 632–642. (CCF B, EI)

[C044] Peishuo Liu, Cangqi Zhou*, Jing Zhang, Qianmu Li, & Dianming Hu. (Dec. 1-4, 2023). Hierarchical graph contrastive learning via debiasing noise samples with adaptive repelling ratio. In Proceedings of 23rd IEEE International Conference on Data Mining (ICDM-2023) , Shanghai, China, pp. 418–427. (CCF B, EI)

[C043] Yingjie Xie, Qi Yan, Cangqi Zhou*, Jing Zhang*, & Dianming Hu. (Oct. 6-8, 2023). Heterogeneous graph contrastive learning with dual aggregation scheme and adaptive Augmentation. In Proceedings of the 7th APWeb-WAIM International Joint Conference on Web and Big Data (APWeb-WAIM-2023), Wuhan, China, pp. 124–138. (CCF C, EI)

[C042] Youxuan Wang, Shunmei Meng*, Qi Yan, & Jing Zhang*. (Oct. 6-8, 2023). Multi-relational heterogeneous graph attention networks for knowledge-aware recommendation. In Proceedings of the 7th APWeb-WAIM International Joint Conference on Web and Big Data (APWeb-WAIM-2023), Wuhan, China, pp. 108–123. (CCF C, EI)

[C041] Peishuo Liu, Cangqi Zhou*, Xiao Liu, Jing Zhang, & Qianmu Li. (Sep. 26-28, 2023). Multi-granularity contrastive learning for graph with hierarchical pooling. In Proceedings of the 32nd International Conference on Artificial Neural Networks (ICANN-2023), Heraklion city, Crete, Greece, pp. 499–511. (CCF C, EI)

[C040] Xiao Liu, Shunmei Meng, Qianmu Li, Xiaodong Xu, Lianyong Qi, Wanchun Dou, Jing Zhang, & Xuyun Zhang. (Jul. 2-8, 2023). Disentangled hypergraph collaborative filtering for social recommendation. In Proceedings of the 2023 International Conference on Web Services (ICWS-2023), Chicago, USA, pp. 475–482. (CCF B, EI)

[C039] Nianshang Tian, Ming Wu, Jiqiong Jiang, & Jing Zhang*. (Nov. 20-Dec. 1, 2022). Learning from crowds with mutual correction-based co-training. In Proceedings of the 13th IEEE International Conference on Knowledge Graph (ICKG-2022), Hybrid Conference, Orlando, Florida, USA, pp. 257–264. (EI)

[C038] Meilin Cao, Jiqiong Jiang, & Jing Zhang*. (Oct. 31-Nov. 2, 2022). Multi-instance active learning for relation extraction. In Proceedings of the 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2022), Virtually, 446–450. (CCF C, EI)

[C037] Cangqi Zhou, Yuxiang Wang, Jing Zhang*, Jiqiong Jiang, & Dianming Hu. (Oct. 17-21, 2022). End-to-end modularity-based community co-partition in bipartite networks. In Proceedings of the 31th ACM International Conference on Information and Knowledge Management (CIKM-2022), Hybrid Conference, Atlanta, Georgia, USA, 2711–2720. (CCF B, EI)

[C036] Yuxiang Wang, Cangqi Zhou*, Jing Zhang, & Qianmu Li. (July 18-23, 2022). Attributed graph clustering with double contrastive projector. In Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN-2022), Padua, Italy, 8 pages. (CCF C, EI)

[C035] Cangqi Zhou, Sunyue Xu, Hao Ban, & Jing Zhang*. (May 16-19, 2022). Neural topic modeling with Gaussian mixture model and householder flow. In Proceedings of 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2022), Chengdu, China, pp. 417–428. (CCF C, EI)

[C034] Sunyue Xu & Jing Zhang*. (Feb. 22-Mar. 1, 2022). Crowdsourcing with meta-knowledge transfer (student abstract). In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022), Vancouver, BC, Canada, pp. 13095–13096. (CCF A, EI)

[C033] Ming Wu, Qianmu Li*, Jing Zhang*, & Jun Hou. (Feb. 18-21, 2022). Label aggregation with clustering for biased crowdsourced labeling. In Proceedings of 14th International Conference on Machine Learning and Computing , Guangzhou, China, pp. 165–169. (EI)

[C032] Cangqi Zhou, Hui Chen, Jing Zhang*, Qianmu Li, & Dianming Hu. (Feb. 21-25, 2022). AngHNE: Representation learning for bipartite heterogeneous networks with angular loss. In Proceedings of the 15th International Conference on Web Search and Data Mining (WSDM-2022), Phoenix, Arizona, USA, pp. 1470–1478. (CCF B, EI)

[C031] Qingren Wang, Liang Sun, Jie Cui, & Jing Zhang. (Dec. 20-22, 2021). SA_3AM: A novel sentiment analysis approach integrating three-level attention mechanism. In Proceedings of the 19th International Conference on Smart City (SmartCity-2021), Haikou, Hainan, China, pp. 1769–1776. (EI)

[C030] Yujie Yin, Feng Zhang, Jing Zhang, & Jipeng Qiang. (Dec. 20-22, 2021). Personalized English lexical simplification for Chinese. In Proceedings of the 19th International Conference on Smart City (SmartCity-2021), Haikou, Hainan, China, pp. 1777–1782. (EI)

[C029] Cangqi Zhou, Jinling Shang, Jing Zhang*, Qianmu Li, & Dianming Hu. (Dec. 7-10, 2021). Topic-attentive encoder-decoder with pre-trained language model for keyphrase generation. In Proceedings of the 21st International Conference on Data Mining (ICDM-2021), Auckland, New Zealand, pp. 1529–1534. (CCF B, EI)

[C028] Mengxi Li, Jing Zhang*, Lixia Chen, Yu Fu, & Cangqi Zhou. (Nov. 1-5, 2021). HPEMed: Heterogeneous network pair embedding for medical diagnosis. In Proceedings of 16th Chinese Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW-2021), Xiangtan, Hunan, China, pp. 364–375. (EI)

[C027] Yu Lei & Jing Zhang*. (Nov. 1-5, 2021). Capsule graph neural networks with EM routing. In Proceedings of 30th ACM International Conference on Information and Knowledge Management (CIKM-2021), Virtual Conference, Gold Coast, Queensland, Australia, pp. 3191–3195. (CCF B, EI)

[C026] Hui Chen, Cangqi Zhou, Jing Zhang & Qianmu Li. (Jul. 18-22, 2021). Heterogeneous graph embedding based on edge-aware neighborhood convolution. In Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN-2021), Virtual Conference, 8 pages. (CCF C, EI)

[C025] Mengru Dong, Shunmei Meng, Lixia Chen, & Jing Zhang*. (Dec. 11-12, 2020). Personalized medical diagnosis recommendation based on neutrosophic sets and spectral clustering. In Proceedings of the 10th EAI International Conference on Cloud Computing (CloudComp-2020) (LNICST 363), Qufu, Shandong, China, pp. 160–174. (EI)

[C024] Meilin Cao, Jing Zhang*, Sunyue Xu, & Zijian Ying. (Dec. 11-12, 2020). Knowledge graphs meet crowdsourcing: A brief survey. In Proceedings of the 10th EAI International Conference on Cloud Computing (CloudComp-2020) (LNICST 363), Qufu, Shandong, China, pp. 3–17. (EI)

[C023] Yanhui Peng & Jing Zhang*. (Nov. 17-20, 2020). LineaRE: Simple but powerful knowledge graph embedding for link prediction. In Proceedings of 20th IEEE International Conference on Data Mining (ICDM-2020), Sorrento, Italy (Virtual Conference), pp. 422–431. (CCF B, The first author is my Master Student, Student Travel Awards, EI)

[C022] Yanhui Peng, Jing Zhang*, Cangqi Zhou, & Jian Xu. (Aug. 9-11, 2020). Embedding-based entity alignment using relation structural similarity. In Proceedings of 11th IEEE International Conference on Knowledge Graph (ICKG-2020), Nanjing, China, pp. 123–130. (EI)

[C021] Huailong Dong, Bowen Zhu, & Jing Zhang*. (Feb. 12-15, 2020). A cost-sensitive active learning for imbalance data with uncertainty and diversity combination. In Proceedings of 12th International Conference on Machine Learning and Computing, Shenzhen, China, pp. 218–224. (EI)

[C020] Jing Zhang, Huihui Wang, Shunmei Meng, & Victor S. Sheng. (Feb. 7-12, 2020). Interactive learning with proactive cognitive enhancement for crowd workers. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-2020), New York, USA, pp. 540–547. (CCF A, EI)

[C019] Kaisheng Gao, Jing Zhang*, & Cangqi Zhou. (Nov. 26-30, 2019). Semi-supervised graph embedding for multi-label graph node classification. In Proceedings of the 20th International Conference on Web Information Systems Engineering (WISE-2019), Hong Kong, China, pp. 555–567. (CCF C, EI)

[C018] Huihui Wang, Shunmei Meng, Jinbiao Yu, & Jing Zhang*. (Nov. 8-11, 2019). Fast classification algorithms via distributed accelerated alternating direction method of multipliers. In Proceedings of the 2019 International Conference on Data Mining (ICDM-2019), Beijing, China, pp. 1354–1359. (CCF B, EI)

[C017] Bowen Zhu, Huailong Dong, & Jing Zhang*. (Oct. 17-20, 2019). Car sales prediction using gated recurrent units neural networks with reinforcement learning. In Proceedings of the 2019 International Conference on Intelligence Science and Big Data Engineering (IScIDE-2019), Nanjing, Jiangsu, China, pp. 312–324. (EI)

[C016] Xiao Dong, Qianmu Li, Jun Hou, Jing Zhang, & Yaozong Liu. (Apr. 4-9, 2019). Security risk control of water power generation industrial control network based on attack and defense map. In Workshop on Big Data in Water Resources, Environment, and Hydraulic Engineering at The Fifth IEEE International Conference on Big Data Service and Applications, San Francisco East Bay, California, USA, pp. 232–236.

[C015] Victor S. Sheng & Jing Zhang*. (Jan. 2019). Machine learning with crowdsourcing: A brief summary of the past research and future directions. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-2019, Senior Member Track), Honolulu, Hawaii, USA, pp. 9837–9843. (CCF A)

[C014] Jing Zhang & Xindong Wu. (Aug. 19-23, 2018). Multi-label inference for crowdsourcing. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2018), London, United Kingdom, pp. 2738–2747. (Research Track, CCF A, EI)

[C013] Yanjun Song, Jing Zhang, Han Yan, & Qianmu Li. (Jun. 8-10, 2018). Multi-class imbalanced learning with one-versus-one decomposition: An empirical study. In the 4th International Conference on Cloud Computing and Security (ICCCS-2018), Revised Selected Papers, Part III, Haikou, Hainan, China, pp. 617–628. (EI)

[C012] Ming Wu, Qianmu Li, Jing Zhang, Shicheng Cui, Deqiang Li & Yong Qi. (Nov. 24-26, 2017). A robust inference algorithm for crowdsourced categorization. In Proceedings of the 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE-2017), Nanjing, Jiangsu, China. (EI)

[C011] Jing Zhang, Victor S. Sheng, & Tao Li. (Aug. 7-11, 2017). Label aggregation for crowdsourcing with bi-layer clustering. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2017), Tokyo, Japan, pp. 921–924. (CCF A, EI)

[C010] Xiaoqiang Xu, Jing Zhang & Qianmu Li. (Jun. 13-16, 2016). Equalized interval centroid based watermarking scheme for stepping stone traceback. In Proceedings of the IEEE 2016 International Conference on Data Science in Cyberspace (IDSC-2016), Changsha, Hunan, China, pp. 109–117. (EI)

[C009] Zhenyu Shu, Victor S. Sheng, Yang Zhang, Dianhong Wang, Jing Zhang, & Heng Chen. (Dec. 9-11, 2015). Integrating active learning with supervision for crowdsourcing generalization. In Proceedings of the IEEE 2015 International Conference on Machine Learning and Applications (ICMLA-2015), Miami, Florida, USA, pp. 232–237. (EI)

[C008] Bryce A. Nicholson, Victor S. Sheng, Jing Zhang, Zhiheng Wang, & Xuefeng Xian. (Oct. 25-31, 2015). Improving label accuracy by filtering low-quality workers in crowdsourcing. In Advances in Artificial Intelligence and Soft Computing (Proceedings of the 14th Mexican International Conference on Artificial Intelligence (MICAI-2015), Cuernavaca, Morelos, Mexico, Part I), Lecture Notes in Computer Science, vol. 9413, Springer, pp. 547–559. (EI)

[C007] Jing Zhang, Victor S. Sheng, Jian Wu, Xiaoqin Fu, & Xindong Wu. (Oct. 19-23, 2015). Improving label quality in crowdsourcing using noise correction. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM-2015), Melbourne, Australia, pp. 1931–1934. (CCF B, EI)

[C006] Bryce A. Nicholson, Jing Zhang, Victor S. Sheng,& Zhiheng Wang. (Oct. 19-21, 2015). Label noise correction methods. In Proceedings of the 2015 IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA-2015), Paris, France, pp. 94–102. (EI)

[C005] Bryce A. Nicholson, Victor S. Sheng, & Jing Zhang. (Sept. 27-30, 2015). Noise correction of image labeling in crowdsourcing. In Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP-2015), Quebec City, Canada, pp. 1458–1462. (EI)

[C004] Jian Wu, Victor S. Sheng, Jing Zhang, Pengpeng Zhao, & Zhiming Cui. (Oct. 27-30, 2014). Multi-label active learning for image classification. In Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP-2014), Paris, France, pp.5227–5231. (EI)

[C003] Jing Zhang, Xindong Wu, & Victor S. Sheng. (Aug. 25-28, 2013). A threshold method for imbalanced multiple noisy labeling. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM-2013), Niagara Falls, Canada, pp. 61–65. (EI)

[C002] Jing Zhang, Xindong Wu, & Victor S. Sheng. (Jul. 14-18, 2013). Imbalanced multiple noisy labeling for supervised learning. In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI-2013), Bellevue, Washington, USA, pp. 1651–1652. (CCF A, EI)

[C001] Jing Zhang, Gongqing Wu, Xuegang Hu, & Xindong Wu. (Sep. 20-23, 2012). A distributed cache for Hadoop distributed file system in real-time cloud services. In Proceedings of the 13th ACM/IEEE International Conference on Grid Computing (GRID-2012), Beijing, China, pp.12–21. (CCF C, EI)

Teaching Research (2)

[T002] 张静. (2025). 面向网络空间安全专业的数据结构实验案例设计. 计算机教育, 8. (CCF 推荐中文核心期刊T2类)

[T001] 张静, 蔡志成, 孟顺梅, 李千目. (Sep. 2019). 大规模分布式机器学习本科生科研训练项目群建设探讨. 计算机教育, 297: 92–95. (CCF 推荐中文核心期刊T2类)

Granted Invention Patents (4)

[P04] Jing Zhang, Sunyue Xu, & Jiqiong Jiang. Active crowdsourcing learning method and device based on a time series model of annotator reliability (基于标注者可靠度时序建模的众包主动学习方法和装置). Application No. CN 202210512110.0, China.

[P03] Jian Xu, Mengchi Yu, & Jing Zhang. Label Noise Correction Methods (噪声标签纠正方法). Application No. CN 201910562002.2, China.

[P02] Jing Zhang, Yanhui Peng, & Lixia Chen. Aggregative analysis method for medical consultation information (医疗咨询信息聚合分析方法). Application No. CN 201811211126.8, China.

[P01] Jing Zhang, Jidong Yu, Jun Yan, Xiaoru Wu, & Qingfeng Liu. A load balance method for distributed MRCP servers (一种分布式MRCP服务器负载均衡系统的均衡方法). Application No. CN 200910185900.7, China.

Software

CEKA: An Open Source Tool for Mining the Wisdom of the Crowd.

Academic Services

PC Memeber for International Conferences:
AAAI-2025, ICLR-2025, IJCAI-2025
AAAI-2024, WSDM-2024 (Demo Track), SDM-2024, IJCAI-2024, ICDM-2024, UAI-2024, ICLR-2024
AAAI-2023, ICLR-2023, ICML-2023, KDD-2023, ICDM-2023
IJCAI-2022, ICML-2022, KDD-2022 (Research Track), NeurIPS-2022, ICLR-2022, CIKM-2022, ICDM-2022, SDM-2022, WSDM-2022, PAKDD-2022
AAAI-2021 (Main Track, AISI Track, Student and Poster Track), IJCAI-2021 (Main Track), ICML-2021, KDD-2021, ICLR-2021, WSDM-2021, CIKM-2021, ICDM-2021
ICML-2020, KDD-2020, AAAI-2020, IJCAI-2020, ICDM-2020, CIKM-2020, ECAI-2020, PAKDD-2020, ICAISC-2020, PIC-2020
AAAI-2019, IJCAI-2019, ICDM-2019, PAKDD-2019, BigData-2019 (Workshop on Human-in-the-Loop)
PAKDD-2018, HMData-2018, PIC-2018
WWW-2017, IEEE ICBK-2017(PC login), IEEE DSC-2017, PIC-2017
IEEE DSC-2016, BESC-2016, ES-2016, AAAI HCOMP-2016, PIC-2016
IEEE PIC-2015

Finance Chair:
ICKG-2023

Workshop Chair:
Workshop: Weak Label Learning and Applications in ICCCS-2018

Reviewer for International Journals:
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Cybernetics
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Emerging Topics in Computational Intelligence
IEEE Transactions on Systems, Man, and Cybernetics: Systems
IEEE Transactions on Cognitive and Developmental Systems
IEEE Transactions on Industrial Informatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Architecture and Code Optimization
ACM Transactions on Knowledge Discovery in Data
IEEE/CAA Journal of Automatica Sinica
Journal of Machine Learning Research
Information Sciences
Information Fusion
Machine Learning
Pattern Recognition
Knowledge and Information Systems
Future Generation Computer Systems
Journal of Informetrics
Artificial Intelligence Review
The Computer Journal
Journal of Informetrics
Journal of Grid Computing
Journal of Cloud Computing
Pattern Recognition Letters
Concurrency and Computation: Practice and Experience
Engineering Applications of Artificial Intelligence
Computers & Security
Complexity
Tsinghua Science and Technology
IEEE Access
Heliyon
IET Networks
CMC-Computers Materials & Continua
Scientia Iranica
Financial Innovation
Journal of Healthcare Engineering
PLOS ONE
Progress in Artificial Intelligence

Reviewer for Chinese Journals:
自动化学报
计算机研究与发展
系统工程学报