Di Wu (吴 迪)
Ph.D, Professor, Ph.D. Supervisor
College of Computer and Information Science, Southwest University, China （中文主页）

Location: Building 25, No.2 Tiansheng Road, Beibei District, Chongqing, China
Biography | Submission | Publications | Projects | Education and Career | Services | Awards

Email: wudi.cigit@gmail.com;     
[Google Scholar] [GitHub] [ResearchGate] [Publons] [ORCID] [Wechat微信]

Biography

 I received the Ph.D. degree in Computer Science from Chongqing Institute of Green and Intelligent Technology (CIGIT), Chinese Academy of Sciences (CAS), Chongqing, China in 2019. I was a visiting scholar from April 2018 to April 2019 at the University of Louisiana, Lafayette, USA. Currently, I am working in the College of Computer and Information Science, Southwest University, as a Professor. I published over 70 papers, including top journals and conferences like TKDE, TNNLS, TSC, TSMC, TII, ICDM, WWW, IJCAI, etc. Google Scholar cited by more than 2300, H-Index 26. I am serving as an Associate Editor for Neurocomputing (SCI, IF 6) and Frontiers in Neurorobotics (SCI, IF 3.1). I am a member of IEEE. My research interests include:



News
  • [2023.12] Our work regarding Drug Analysis is accepted by AAAI 2024 (CCF-A).
  • [2023.12] Our work regarding Spatiotemporal Signal Recovery is accepted by IEEE TNNLS (IF 10.4, CCF-B).
  • [2023.11] Our work regarding Multi-Metric Latent Feature Analysis is accepted by IEEE TSC (IF 8.1, CCF-A).
  • [2023.11] Our work regarding Recommender System is accepted by ACM TOIS (IF 5.6, CCF-A).
  • [2023.10] Our work regarding Online Learning is accepted by IEEE TKDE (IF 8.9, CCF-A).
  • [2023.08] Our work regarding Streaming Data Analysis is accepted by Information Sciences (CCF-B).
  • [2023.07] Our work regarding Graph-incorporated Latent Factor Analysis is accepted by IEEE TETC (IF 5.9).
  • [2023.06] Our work regarding Multi-metric Autoencoder is accepted by ECML-PKDD (CCF-B).
  • [2023.05] Our work regarding Recommender Systems is accepted by IEEE TCE (IF 4.414).
  • [2023.04] Our work regarding Energy Internet is accepted by IEEE (IF 11.019, CCF-A).
  • [2023.03] Our work regarding Online Learning is accepted by IEEE TKDE (IF 9.235, CCF-A).
  • [2023.01] Our work regarding Deep Learning-based Recommender Systems is accepted by IEEE (IF 11.019, CCF-A).
  • [2023.01] Our work regarding Counterfactual Explanation Generation is accepted by Information Sciences (CCF-B).
  • [2022.12] My monograph titled ‘Robust Latent Feature Learning for Incomplete Big Data’ is published by Springer .
  • [2022.11] Our work regarding online learning is accepted by AAAI 2023 (CCF-A).
  • [2022.10] Two papers regarding autoencoder-based recommender systems are accepted by IEEE UIC 2022 (CCF-C).
  • [2022.9] One paper regarding refining latent factor analysis is accepted by IEEE SMC 2022 (CCF-C).
  • [2022.09] One paper is accepted by IEEE TNNLS (IF 14.255).
  • [2022.07] One paper is accepted by IEEE TETCI (IF 4.851).
  • [2022.07] One paper is accepted by Information Sciences (IF 8.233).
  • [2022.05] One paper is accepted by IEEE TSC (IF 11.019, CCF-A).
  • [2021.12] I get a "Nomination Award for Excellent Doctoral Dissertation of Wu Wenjun Artificial Intelligence" from "Chinese Association for Artificial Intelligence，CAAI" link

Submission
  • [2023.09] We have organized a special issue on Neurocomputing regarding "Frontiers in Graph Computation: Techniques, Challenges, and Applications”. Welcome to submit via link
  • [2023.09] We have organized a special issue "AI and Data Security for the Industrial Internet”. Welcome to submit via link
  • [2023.04] We have organized "IEEE International Workshop on Incomplete Streaming Data Analysis (ISDA 2023)" on IEEE ICDM 2023. Welcome to submit via link
  • [2022.08] I host a special issue regarding "Security and Privacy Issues in Multi-Source Information Fusion" on International Journal of Computational Science and Engineering (EI/ESCI Journal). Welcome to submit via link
  • [2022.06] I host a special issue regarding "Human-Centric Computing and Equitable Artificial Intelligence" on Frontiers in Neurorobotics (SCI, IF 2.65). Welcome to submit via link
  • [2021.11] I host a special issue regarding "Neurorobotics and Neurological Diseases" on Frontiers in Neurorobotics (SCI, IF 2.65). Welcome to submit via link
  • [2021.08] I host a special issue regarding "Neuroscience-Inspired Intelligent Computingon" on Frontiers in Neurorobotics (SCI, IF 2.65). Welcome to submit via link
  • [2021.07] I host a special issue regarding "Symmetry and Asymmetry Phenomena in Incomplete Big Data Analysis" on Symmetry-Basel (SCI, IF 2.713). Welcome to submit via link

Selected Publications

 (Full list see my [Google Scholar])

2025:

  • Dianlong You, Dongyan Wang, Bingxin Liu, Xiaoyi Ge, Di Wu*, Xindong Wu, Disentangle Representation Learning with Excluding Confounding Bias for causal effect estimation, Knowledge-Based Systems, 2025, Vol.310, PP.112926(1-14). (*Corresponding Author, IF 7.2, 中科院一区) [Link] [PDF]

2024:

  • Di Wu, Wu Sun, Yi He, Zhong Chen, and Xin Luo, MKG-FENN: A Multimodal Knowledge Graph Fused End-to-end Neural Network for Accurate Drug-Drug Interaction Prediction, Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10216-10224, 2024. https://doi.org/10.1609/aaai.v38i9.28887 (Accept rate 23.75%, core-rank A*, CCF-A会议) [Link] [PDF]
  • Di Wu, Yuanpeng Hu, Kechen Liu, Jing Li, Xianmin Wang, Song Deng, Nenggan Zheng, and Xin Luo, An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data, IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, doi: 10.1109/TETCI.2024.3437370 (IF 5.3) [Link] [PDF]
  • Dianlong You, Huigui Yan, Jiawei Xiao, Zhen Chen, Di Wu*, Limin Shen, and Xindong Wu, Online Learning for Data Streams with Incomplete Features and Labels, IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 9, pp.4820-4834, Sept. 2024, doi: 10.1109/TKDE.2024.3374357.(*Corresponding Author, CCF-A，IF 8.9) [Link] [PDF]
  • Jie Yang, Xiaoqi Wang, Guoyin Wang, Qinghua Zhang, Nenggan Zheng, Di Wu*, "Fuzziness-based three-way decision with neighborhood rough sets under the framework of shadowed sets, IEEE Transactions on Fuzzy Systems, Vol 32, No 9, September, pp.4976-4988, 2024, doi: 10.1109/TFUZZ.2024.3399769. (*Corresponding Author, CCF-B, IF 10.7) [Link] [PDF]
  • Jie Yang, Yapeng Li, Guoyin Wang, Zhong Chen, Di Wu*, An End-to-end Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024, doi: 10.1109/TCBB.2024.3486216. (*Corresponding Author, CCF-B) [Link] [PDF]
  • Jie Yang, Zhijun Wang, Guoyin Wang, Yanmin Liu, Yi He, Di Wu*, OSFS-Vague: Online streaming feature selection algorithm based on vague set, CAAI Transactions on Intelligence Technology, PP.1–16, 2024, https://doi.org/10.1049/cit2.12327 (*Corresponding Author, IF 8.4,中科院二区) [Link] [PDF]
  • Jie Yang, Xiaodan Qin, Guoyin Wang, Qinghua Zhang, Shuai Li, Di Wu*, Attribute Reduction for Hierarchical Classification Based on Improved Fuzzy Rough Set, Information Sciences, vol 677, pp.120900, June 2024, https://doi.org/10.1016/j.ins.2024.120900 (*Corresponding Author,中科院一区) [Link] [PDF]
  • Jie Yang, Juncheng Kuang, Guoyin Wang, Qinghua Zhang, Yanmin Liu, Qun Liu, Deyou Xia, Shuai Li, Xiaoqi Wang, Di Wu*, Adaptive three-way KNN classifier using density-based granular balls, Information Sciences, vol 678, pp.120858, September 2024, https://doi.org/10.1016/j.ins.2024.120858 (*Corresponding Author,中科院一区) [Link] [PDF]

2023:

  • Di Wu, Zechao Li, Zhikai Yu, Yi He, and X. Luo, Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery, IEEE Transactions on Neural Networks and Learning Systems, vol.36, no.2, pp.2829-2842, Feb. 2025. doi: 10.1109/TNNLS.2023.3339786. (中科院一区, CCF-B期刊, IF 10.4) [Link] [PDF]
  • Di Wu, Peng Zhang, Yi He, and Xin Luo, MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data, IEEE Transactions on Services Computing, vol. 17, no. 2, pp. 575-588, March-April 2024, doi: 10.1109/TSC.2023.3331570.(中科院一区, CCF-A期刊, IF 8.1) [Link] [PDF]
  • Di Wu, Bo Sun, and Mingsheng Shang, Hyperparameter Learning for Deep Learning-based Recommender Systems, IEEE Transactions on Services Computing, vol. 16, no. 4, pp. 2699-2712, 2023. doi: 10.1109/TSC.2023.3234623. (中科院一区, CCF-A期刊, IF 8.1) [Link] [PDF]
  • Di Wu, Shengda Zhuo, Yu Wang, Zhong Chen, and Yi He, Online Semi-Supervised Learning with Mix-Typed Streaming Features, The 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37(4): 4720-4728. (Accept rate 19.6%, core-rank A*, CCF-A会议) [Link] [PDF]
  • Di Wu, Yi He, and Xin Luo, A Graph-Incorporated Latent Factor Analysis Model for High-Dimensional and Sparse Data, IEEE Transactions on Emerging Topics in Computing, vol. 11, no. 4, pp. 907-917, 2023, doi: 10.1109/TETC.2023.3292866. (中科院二区, IF 5.9) [Link] [PDF]
  • Di Wu, Robust Latent Feature Learning for Incomplete Big Data, Springer Singapore, 2023. doi.org/10.1007/978-981-19-8140-1 (Book) [Link] [PDF]
  • Dianlong You, Jiawei Xiao, Yang Wang, Huigui Yan, Di Wu*, Zhen Chen, Limin Shen, and Xindong Wu, "Online Learning From Incomplete and Imbalanced Data Streams," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 10650-10665, 1 Oct. 2023, doi: 10.1109/TKDE.2023.3250472. (*Corresponding Author, CCF-A，IF 9.235, 中科院一区) [Link] [PDF]
  • Song Deng, Yujia Zhai, Di Wu*, Dong Yue, Xiong Fu, and Yi He, "A Lightweight Dynamic Storage Algorithm with Adaptive Encoding for Energy Internet", IEEE Transactions on Services Computing, vol. 16, no. 5, pp. 3115-3128, Sept.-Oct. 2023, doi: 10.1109/TSC.2023.3262635. (*Corresponding Author, CCF-A, 中科院一区, IF 8.1) [Link] [PDF]
  • Teng Huang, Cheng Liang, Di Wu*, and Yi He, "A Debiasing Autoencoder for Recommender System," IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 3603-3613, Feb. 2024, doi: 10.1109/TCE.2023.3281521.(*Corresponding Author, IF 4.414, 中科院二区) [Link] [PDF]
  • Cheng Liang, Di Wu*, Teng Huang, and Yi He, MMA: Multi-Metric-Autoencoder for Analyzing High-Dimensional and Incomplete Data, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2023. (*Corresponding Author, CCF-B) [Link] [PDF]
  • Dianlong You, Siqi Dong, Shina Niu, Huigui Yan, Zhen Chen, Shunfu Jin, Di Wu*, Xindong Wu, Local causal structure learning for streaming features, Information Sciences, vol 647, pp.119502, 2023. (*Corresponding Author, IF 8.1, 中科院一区, CCF-B) [Link] [PDF]
  • Dianlong You, Shina Niu, Siqi Dong, Huigui Yan, Zhen Chen, Di Wu*, Limin Shen, and Xindong Wu, Counterfactual explanation generation with minimal feature boundary, Information Sciences, vol 625, pp.342-366, 2023. (*Corresponding Author, IF 8.1, 中科院一区, CCF-B) [Link] [PDF]

2022:

  • Di Wu, Xin Luo, Yi He, and MengChu Zhou, A Prediction-sampling-based Multilayer-structured Latent Factor Model for Accurate Representation to High-dimensional and Sparse Data, IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 3845-3858, March 2024, doi: 10.1109/TNNLS.2022.3200009. (中科院一区，IF 14.255) [PDF]
  • Di Wu, Peng Zhang, Yi He, and Xin Luo, A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction, IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 802-814, 2023. doi: 10.1109/TSC.2022.3178543 (中科院一区, CCF-A期刊, IF 11.019) [PDF]
  • Song Deng, Jiantang Zhang, Di Wu*, Yi He, Xiangpeng Xie, and Xindong Wu, A Quantitative Risk Assessment Model for Distribution Cyber Physical System under Cyber Attack, IEEE Transactions on Industrial Informatics, vol. 19, no. 3, pp. 2899-2908, 2023. DOI: 10.1109/TII.2022.3169456 (*Corresponding Author, 中科院一区, IF 11.648) [PDF]
  • Bo Sun, Di Wu*, Mingsheng Shang, and Yi He, Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems. DASFAA 2022, 323-331, 2022. (*Corresponding Author, CCF-B) [PDF]
  • Yuanyi Liu, Jia Chen, and Di Wu*, An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factor Analysis Model, IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), 2022, pp. 854-859. (*Corresponding Author, CCF-C) [PDF]
  • Song Deng, Fulin Chen, Di Wu*, Yi He, Hui Ge, Yuan Ge, Quantitative combination load forecasting model based on forecasting error optimization, Computers and Electrical Engineering, vol 101, pp.108125, 2022. (*Corresponding Author, IF 4.152) [PDF]

2021:

  • Di Wu, Yi He, Xin Luo, and MengChu Zhou, A Latent Factor Analysis-based Approach to Online Sparse Streaming Feature Selection, IEEE Transactions on Systems Man and Cybernetics-Systems, vol. 52, no. 11, pp. 6744-6758, 2022. DOI: 10.1109/TSMC.2021.3096065 (中科院一区, IF 13.451) [PDF]
  • Di Wu, Mingsheng Shang, Xin Luo, and Zidong. Wang, An L1-and-L2-Norm-Oriented Latent Factor Model for Recommender Systems, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5775-5788, 2022. doi: 10.1109/TNNLS.2021.3071392. (中科院一区，IF 14.255) [PDF]
  • Di Wu and Xin Luo, Robust Latent Factor Analysis for Precise Representation of High-dimensional and Sparse Data, IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, 2021. DOI: 10.1109/JAS.2020.1003533. (中国科技期刊卓越行动计划世界一流重点建设期刊, IF=6.171，中科院一区) [PDF]

2020:

  • Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin. Wang, and Xindong Wu, A Data-Characteristic-Aware Latent Factor Model for Web Service QoS Prediction, IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2525-2538, 2022. (CCF推荐A类期刊，IF 9.235，Highly cited papers) [PDF]
  • Di Wu, Long Jin, and Xin Luo, PMLF: Prediction-Sampling-based Multilayer-Structured Latent Factor Analysis, In proceeding of the 2020 IEEE International Conference on Data Mining, ICDM, 2020. (Regular paper, Accept rate 9.8%,core-rank A*, CCF推荐B类会议) [PDF]
  • Ye Yuan, Xin Luo, Mingsheng Shang, and Di Wu. A generalized and fast-converging nonnegative latent factor model for predicting user preferences in recommender systems. Proceedings of the International World Wide Web Conference (WWW), 498-507, 2020. (CCF 推荐 A 类会议) [PDF]
  • Yi He, Baijun Wu, Di Wu, Ege Beyazit, Sheng Chen, Xindong Wu, Toward Mining Capricious Data Streams: A Generative Approach, IEEE Transactions on Neural Networks and Learning Systems, 2020. DOI: 10.1109/TNNLS.2020.2981386. (中科院一区, IF 8.793)
  • Yi He, Baijun Wu, Di Wu, and Xindong Wu. On partial multi-task learning. Proceedings of the European Conference on Artificial Intelligence (ECAI), 1174-1181, 2020. (CCF推荐B类会议)

2019:

  • Di Wu, Qiang He, Xin Luo, Mingsheng Shang, Yi He, and Guoyin Wang, A posterior-neighborhood-regularized latent factor model for highly accurate web service QoS prediction, IEEE Transactions on Services Computing, vol. 15, no. 2, pp. 793-805, 1 March-April 2022. (中科院一区, CCF-A期刊, IF 11.019, Highly cited papers) [PDF]
  • Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and MengChu Zhou, A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems, IEEE Transactions on Systems Man and Cybernetics: Systems, vol. 51, no. 7, pp. 4285-4296, 2021, DOI:10.1109/TSMC.2019.2931393. (中科院一区, IF 9.309, Highly cited papers) [PDF]
  • Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and Xindong Wu, A Data-Aware Latent Factor Model for Web Service QoS Prediction, In proceeding of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD, 2019. (CCF推荐C类会议, Accept rate 24.1%) [PDF]
  • Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and Xindong Wu, Online Feature Selection with Capricious Streaming Features: A General Framework, In proceeding of the 2019 IEEE international conference on big data, Bigdata, 2019. (CCF推荐C类会议, Accept rate 38.3%) [PDF]
  • Yi He, Baijun Wu, Di Wu, Ege Beyazit, Sheng Chen, and Xindong Wu, Online Learning from Capricious Data Streams: A Generative Approach, In proceeding of the 28th International Joint Conference on Artificial Intelligence, IJCAI, 2019. (CCF推荐A类会议)
  • Xin Luo, Mengchu Zhou, Shuai Li, Di Wu, Zhigang Liu, and Mingsheng Shang, Algorithms of Unconstrained Non-negative Latent Factor Analysis for Recommender Systems, IEEE Transactions on Big Data, 2019, doi: 10.1109/TBDATA.2019.2916868.

2018:

  • Di Wu, Xin Luo, Guoyin Wang, Mingsheng Shang, Ye Yuan, and Huyong Yan, A Highly-Accurate Framework for Self-Labeled Semi-Supervised Classification in Industrial Applications, IEEE Transactions on Industrial Informatics, 2018, 14 (3): 909-920. (中科院一区, IF 9.112) [PDF]
  • Di Wu, aMinsheng Shang, Xin Luo, Ji Xu, Huyong Yan, Weihui Deng, and Guoyin Wang, Self-training semi-supervised classification based on density peaks of data, Neurocomputing, 2018, 275:180-191. (中科院二区, IF 4.438) [PDF]
  • Yi He, Di Wu, Ege Beyazit, Xiaoduan Sun, and Xindong Wu. Supervised Data Synthesizing and Evolving–A Framework for Real-World Traffic Crash Severity Classification. In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence, ICTAI, 2018. (CCF推荐C类会议)

2017 and before:

  • Di Wu, Huyong Yan, Mingsheng Shang, Kun Shan, and Guoyin Wang, Water eutrophication evaluation based on semi-supervised classification: A case study in Three Gorges Reservoir, Ecological Indicators, 2017, 81: 362-372. (中科院二区, IF 4.229) [PDF]



Education and Career
  • 2022/07-Present        Southwest University.            Professor
  • 2012/07-2022/06        Chongqing Institute of Green and Intelligent Technology, CAS.            Associate Professor/Assistant Professor
  • 2018/04-2019/04        University of Louisiana at Lafayette, USA         Visiting Scholar (Supervisor:Prof.Xindong Wu)
  • 2015/09-2019/06        Chongqing Institute of Green and Intelligent Technology, CAS.          Ph.D (Supervisor:Prof.Guoyin Wang)
  • 2009/09-2012/06        Chongqing University.          M.E. (Supervisor:Prof. Tao Zhu)
  • 2005/09-2009/06        Nanjing University of Science and Technology.          B.S.

Projects
  • 2023 China's energy security risk identification and strategic path optimization technology under the global coupling of coal, oil, gas, and electricity, ¥1,200,000, PI| 全球煤油气电耦合下我国能源安全风险识别与战略路径优化技术研究, 国家电网总部科技项目, 120/407万元, 主持.
  • 2022 National Natural Science Foundation of China, ¥580,000, PI| 国家自然科学基金面上项目, 国家自然科学基金委员会, 58万元, 主持.
  • 2022 Load forecasting based on multi-source information fusion with Artificial intelligence, ¥161,700, PI| 基干多源信息融合的人工智能负荷预测技术研究, 国网江苏省南通供电公司, 16.17万元, 主持.
  • 2020 Enterprise Research Project regarding Evolution Model of Energy Strategy, ¥590,000, PI| 能源战略演变模型开发研究, 国网能源研究院有限公司, 59万元, 主持.
  • 2020 Chinese Academy of Sciences (CAS) “Light of West China” Program, ¥150,000,PI| 西部青年学者, 中国科学院, 15万元, 主持.
  • 2019 Natural Science Foundation of Chongqing (China), ¥100,000, PI| 重庆市自然科学基金面上项目，重庆市科技局, 10万元, 主持.
  • 2018 National Natural Science Foundation of China, ¥240,000, PI| 国家自然科学青年基金, 国家自然科学基金委员会, 24万元, 主持.
  • 2016 Application development project of Chongqing (China), ¥750,000, PI|重庆市应用开发计划项目课题, 重庆市科技局, 75.5万元, 主持.

Services

Editor:


Membership:

  • Program Committee Member for AAAI 2023,2024
  • Program Committee Member for ECML-PKDD 2021,2022,2023
  • Program Committee Member for CIKM 2023
  • Workshop Chair for IEEE ICDM 2023
  • Session Chair for IEEE ICNSC 2022 [Link]


Journal Reviewer:

  • IEEE Transactions on Neural Networks and Learning Systems(TNNLS)
  • IEEE Transactions on Systems Man and Cybernetics: Systems(TSMC)
  • IEEE Transactions on Human-Machine Systems(THMS)
  • IEEE Transactions on Intelligent Transportation Systems(TITS)
  • IEEE Transactions on Services Computing(TSC)
  • IEEE Transactions on Industrial Informatics(TII)
  • IEEE Transactions on Automation Science and Engineering(TASE)
  • IEEE Transactions on Computational Social Systems(TCSS)
  • IEEE/CAA Journal of Automatica Sinica (JAS)
  • Neurocomputing
  • Advanced Engineering Informatics
  • Swarm and Evolutionary Computation
  • European Journal of Operational Research
  • International Journal of Intelligent Systems
  • Journal of Ambient Intelligence and Humanized Computing
  • ......

Awards
  • 2024, "Representation Learning Theory and Optimization Methods for Large- Scale Time-Varying Graph，December 2024, Second Prize of the Natural Science Award of the 2024 Chinese Society of Automation Science and Technology Award, Luo Xin, Li Weisheng, Zeng Nianyin, Wu Di, Wang Zidong | 超大规模时变图表示学习理论与优化方法，2024年12月，2024中国自动化学会科学技术奖-自然奖科学奖二等奖，罗辛、李伟生、曾念寅、吴迪、王子栋
  • 2024, "Key technology of artificial intelligence load forecasting based on multi-source information fusion, awarded the third prize of the Science and Technology Progress Award of State Grid Jiangsu Electric Power Co., Ltd. in December 2024, with Tang Xianghua, Wu Di, Wang Jiayu, Zhang Youran, Mao Yanfang | 基于多源信息融合的人工智能负荷预测关键技术，2024年12月，2024年度国网江苏省电力有限公司科学技术奖-科技进步奖三等奖，汤向华、吴迪、汪家钰、张悠然、毛艳芳
  • 2024, "Key technology of artificial intelligence load forecasting based on multi-source information fusion, awarded the third prize of Jiangsu Province Electrical Science and Technology Award on December 28, 2024, with Tang Xianghua, Wu Di, Wang Dong, Wang Jiayu, Zhang Youran, Mao Yanfang, Xu Xiaoyi, Yan Yiran| 基于多源信息融合的人工智能负荷预测关键技术，2024.12.28，2023年度江苏省电工科学技术奖三等奖，汤向华、吴迪、王栋、汪家钰、张悠然、毛艳芳、徐晓轶、严怡然
  • 2021, "Nomination Award for Excellent Doctoral Dissertation of Chinese Association for Artificial Intelligence (CAAI)| 中国人工智能学会优秀博士学位论文提名奖
  • 2020, Excellent Doctoral Dissertation of Chongqing, China| 重庆市优秀博士论文
  • 2020, Excellent Papers of the First Academical Conference of Science and Technology in Sichuan and Chongqing (Third Prize), China | 首届川渝科技学术大会优秀论文(三等奖)
  • 2018, President Award for Excellent Student of Chinese Academy of Sciences, China | 中国科学院院长优秀奖

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