Jing Zhang, Ph. D.
Associate Professor
, IEEE Senior Member ('19)
School of Computer Science and Engineering,
Nanjing University of Science and Technology (NJUST),
200 Xiaolingwei Street, Nanjing, Jiangsu 210094, P. R. China

Office: Room 2020, Floor 2, Computing and Automation Hall
E-mail: jzhang@njust.edu.cn
Office Hours: No office hour in 2019.


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Brief Biography

I was born in a small beautiful warm city Wuhu located in the middle east of China. I obtained my B.S. degree in Engineering from Anhui University, Hefei, China, in 2003, my M.S. degree in Computer Science from the Graduate University of Chinese Academy of Sciences, Beijing, China, in 2006, and my Ph.D. degree in Computer Science from Hefei University of Technology, Hefei, China, in 2015, under the supervision by Professor Xindong Wu. After obtaining my master's degree, I worked as a Software Engineer/Senior Software Engineer/R&D Manager at Anhui USTC iFLYTEK Co. Ltd., a Chinese public listed IT & AI company, where I spent more than five years in designing and implementing various large-scale systems. I started my Ph.D. study in September 2012, focusing on data mining and machine learning. During my Ph.D. study, I worked as a Visiting Research Scholar 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, in June 2015. I was promoted as an Associate Professor in July 2017. I worked as a Visiting Scholar at the Faculty of Engineering and Information Technology in 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.

Research Interests

  • Data Mining and Machine Learning
  • Distributed Computing and Systems
  • Crowdsourcing and Human-Computer Interaction
  • Software Engineering for Applications
  • Teaching

    Undergraduate

  • Intelligent Analysis and Decision-Making for Complex Engineering Problems (Fall 2020)
  • Compilers: Principles, Techniques and Implementation (with Course Project) (Spring 2020, Spring 2018, Spring 2017)
  • Software Architecture (Spring 2018)
  • Software Project Management (Fall 2016)
  • An Introduction to Computational Thinking (Fall 2015)
  • Graduate

  • Software Modeling Training (Spring 2016)
  • Data Mining in Cyberspace Security (Fall 2020)
  • Funding & Awards

  • 2020 National Natural Science Foundation of China (NSFC) (General Project) (No. 62076130, 2021.1–2024.12, PI)
    Title: Machine Learning with Crowdsourced Annotated Data
  • 2018 National Natural Science Foundation of China (NSFC) (Fostering Project of the Major Research Plan) (No. 91846104, 2019.1–2021.12, PI)
    Title: Multi-Source Heterogeneous Fusion and Knowledge Learning for Big Crowdsourced Data
  • 2018 The Sponsorship of Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-Aged Teachers and Presidents ($1,800/m×12) for visiting at the University of Pittsbugh. (2019.3–2020.2)
  • 2017 Information Fusion (ELSEVIER) Outstanding Reviewing Award.
  • 2017 China Postdoctoral Science Foundation (Special Funding) (No. 2017T100370, 2017.1–2018.12)
    Title: Learning from Crowdsourced Labeled Data with Instance Feature Fusion
  • 2016 National Natural Science Foundation of China (NSFC) (Youth) (No. 61603186, 2017.1–2019.12, PI)
    Title: Ground Truth Inference and Supervised Classification with Crowdsourced Labeling
  • 2016 Natural Science Foundation of Jiangsu Province, China (Youth) (No. BK20160843, 2016.7–2019.6, PI)
    Title: Active Learning with Crowdsourced Labeling
  • 2016 China Postdoctoral Science Foundation (Class One) (No. 2016M590457, 2016.1–2017.12)
    Title: Supervised Learning for Image Classification with Crowdsourced Labeling
  • 2016 Postdoctoral Science Foundation of Jiangsu Province, China (Class C) (No. 1601199C, 2016.1–2017.12)
    Title: Supervised Learning with Enhanced Interactive Crowdsourced Labeling
  • 2016 The Open Project of Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (No. 30916014107, 2016.7–2018.6, PI)
    Title: Video Image Crowdsouced Labeling and Supervised Learning
  • 2015 The Start-up Funding of Nanjing University of Science and Technology (2015.7–2017.6)
  • 2014 National Ministry of Education, China National Scholarship for Ph. D. Students
  • 2013 China Scholarship Council (CSC) scholarship ($1,600/m×12) for visiting at the University of Central Arkansas. (2013.9–2014.9)
  • Publications [56]

    Note: The JCR rankings are those reported by National Science Library, Chinese Academy of Sciences for major categority (engineering technology) in the years that the articles are published. 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 (32)

  • [J032] Jing Zhang & Xindong Wu. (Online 2019). Multi-Label Truth Inference for Crowdsourcing Using Mixture Models. IEEE Transactions on Knowledge and Data Engineering. DOI: 10.1109/TKDE.2019.2951668 (SCI <2018 IF = 3.857>, CCF A, JCR 2)
  • [J031] Yanhui Peng, Jing Zhang*, Cangqi Zhou, & Shunmei Meng. (Online 2020). Knowledge Graph Entity Alignment Using Relation Structural Similarity. Journal of Database Management, 8: 106843–106854. (SCIE, The first author is my Master Student)
  • [J030] Cangqi Zhou, Hao Ban*, Jing Zhang, Qianmu Li, & Yinghua Zhang*. (June 2020). Gaussian Mixture Variational Autoencoder for Semi-supervised Topic Modeling. IEEE Access, 8: 106843–106854. (SCIE <2018 IF = 4.098>, JCR 2)
  • [J029] Qianmu Li, Yanjun Song, Jing Zhang*, & Victor S. Sheng. (June 2020). Multiclass Imbalanced Learning with One-Versus-One Decomposition and Spectral Clustering. Expert Systems with Applications, 147: 1–14. (SCIE <2018 IF = 4.292>, JCR 2)
  • [J028] Ming Wu, Xiaochun Yin, Qianmu Li*, Jing Zhang, Xinqi Feng, Qi Cao, & Haiyuan Shen. (April 2020). Learning deep networks with crowdsourcing for relevance evaluation. EURASIP Journal on Wireless Communications and Networking, 82: 1–11. (SCI <2018 IF = 1.592>, JCR 3)
  • [J027] Jianhan Pan, Teng Cui, Thuc Duy Le, Xiaomei Li, & Jing Zhang. (March 2020). Multi-Group Transfer Learning On Multiple Latent Spaces For Text Classification. IEEE Access, 8: 64120–64130. (SCIE <2018 IF = 4.098>, JCR 2)
  • [J026] Jian Wu, Victor S. Sheng*, Jing Zhang, Hua Li, Tetiana Dadakova, Christine Swisher, Zhiming Cui, & Pengpeng Zhao*. (March 2020). Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise. ACM Computing Surveys, 53(2): 28. (SCI <2018 IF = 6.131>, JCR 1)
  • [J025] Milad Taleby Ahvanooey, Qianmu Li, Xuefang Zhu, Mamoun Alazab, & Jing Zhang. (March 2020). ANiTW: A Novel Intelligent Text Watermarking Technique for Forensic Identification of Spurious Information on Social Media. Computers and Security, 90: 101702. (SCI <2018 IF = 3.062>, JCR 3)
  • [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. (SCIE <2018 IF = 0.711>, JCR 4)
  • [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. (SCIE <2018 IF = 1.167>, JCR 4)
  • [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. (SCI <2018 IF = 11.683>, CCF B, JCR 1)
  • [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. (SCIE <2018 IF = 0.776>, JCR 4)
  • [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. (SCI <2018 IF = 3.857>, CCF A, JCR 2)
  • [J019] Victor S. Sheng, Jing Zhang*, Bin Gu*, & Xindong Wu. (July 2019). Majority Voting and Pairing with Multiple Noisy Labeling. IEEE Transactions on Knowledge and Data Engineering, 31(7): 1355–1368. (SCI <2018 IF = 3.857>, CCF A, JCR 2)
  • [J018] Qianmu Li, Shunmei Meng, Shuo Wang, Jing Zhang, & Jun Hou. (April 2019). CAD: Command-level Anomaly Detection forVehicle-Road Collaborative Charging Network. IEEE Access, 7: 34910–34924. (SCIE <2018 IF = 4.098>, JCR 2)
  • [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. (SCIE <2018 IF = 4.098>, JCR 2)
  • [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. (SCIE <2018 IF = 4.098>, JCR 2)
  • [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. (SCI <2018 IF = 11.683>, CCF B, JCR 1)
  • [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. (SCIE <2018 IF = 4.292>, CCF C, JCR 2)
  • [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. (SCI <2017 IF = 1.087>, JCR 4)
  • [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. (SCIE <2017 IF = 3.977>, CCF B, JCR 2)
  • [J011] Lin Cui, Dechang Pi, & Jing Zhang.(May 2017). DMFA-SR: Deeper Membership and Friendship Awareness for Social Recommendation. IEEE Access, 5: 8904–8915. (SCIE <2017 IF = 3.557>, JCR 2)
  • [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. (SCI <2017 IF = 4.305>, CCF B, JCR 2)
  • [J009] Bryce Nicholson, Victor S. Sheng, & Jing Zhang. (Dec. 2016). Label Noise Correction and Application in Crowdsourcing. Expert Systems with Applications, 66: 149–162. (SCIE <2016 IF = 3.928>, CCF C, JCR 2)
  • [J008] Jing Zhang, Xindong Wu, & Victor S. Sheng. (Dec. 2016). Learning from Crowdsourced Labeled Data: a Survey. Artificial Intelligence Review, 46(4): 543–576. (SCI <2016 IF = 2.627>, JCR 3)
  • [J007] Jing Zhang, Qianmu Li, & Wei Zhou. (Sept. 2016). HDCache: A Distributed Cache System for Real-Time Cloud Services. Journal of Grid Computing. 14(3): 407–428. (SCIE <2016 IF = 2.766>, CCF C, JCR 3)
  • [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. (SCI <2016 IF = 3.438>, CCF A, JCR 2)
  • [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. (SCIE <2015 IF = 2.450>, CCF A, JCR 2)
  • [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. (SCI <2015 IF = 4.943>, CCF B, JCR 1)
  • [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. (SCI <2015 IF = 2.476>, CCF A, JCR 3)
  • [J002] Haiyan Wang, Lei Zheng, Xueping Xu, & Jing Zhang*. (Sept. 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 (23)

  • [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), Virtual Conference. (Regular paper, accept rate = 9.8%, CCF B, The first author is my Master Student)
  • [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), Nanjing, China, pp 123–130.
  • [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.
  • [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. (Accept rate = 20.6%, 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. (Accept rate = 24.6%, 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. (Accept rate = 18.5%, 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. (April 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, EI)
  • [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, accept rate 181/983=18.4%, 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. (Short Research Paper, accept rate 30%, 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. (Accept rate 33.0%, 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. (Accept rate 25.4%, 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. (Accept rate 28%, 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. (Sept. 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. (Accept rate 29%, CCF C, EI)
  • Teaching Research(1)

  • [T001] 张静, 蔡志成, 孟顺梅, 李千目. (Sept. 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C类)
  • Patents

  • Jing Zhang, Jidong Yu, Jun Yan, Xiaoru Wu, & Qingfeng Liu. A Novel Load Balance Method in a Distributed MRCP Service System. Invention Patent in P.R.China, Application No. 200910185900.7, Granted No. CN 101753558 B.
  • Jing Zhang. A Label Noise Correction Based Method for Crowdsourced Data Quality Improvement. Invention Patent in P.R.China, Application No. 201510754782.2.
  • Jing Zhang, Yanhui Peng, & Lixia Chen. Aggregative Analysis Method for Medical Consultation Information. Invention Patent in P.R.China, Application No. 201811211126.8. (Published and Waiting for Substantive Examination )
  • Jing Zhang & Huailong Dong. Imbalanced Data Classification Based on Active Learning. Invention Patent in P.R.China, Application No. 2020010148859.2. (Published and Waiting for Substantive Examination )
  • Software

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

  • PC Memeber for International Conferences (28):
    AAAI-2021 (Main Track, AISI Track, Student and Poster Track), IJCAI-2021 (Main Track), ICLR-2021, WSDM-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
  • Workshop Chair (1):
    Workshop: Weak Label Learning and Applications in ICCCS-2018
  • Reviewer for International Journals (35):
    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
    ACM Transactions on Architecture and Code Optimization
    ACM Transactions on Knowledge Discovery in Data
    Journal of Machine Learning Research
    Information Sciences
    Information Fusion
    Machine Learning
    Knowledge and Information Systems
    Future Generation Computer Systems
    Artificial Intelligence Review
    The Computer Journal
    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 (1):
    自动化学报
    系统工程学报


  • Copyright © Jing Zhang 2015 - 2020. All rights reserved. This page was last edited on October 12, 2020.