Kun Wang is currently the lecture of the School of Computer Engineering and Science, Shanghai University. She has finished her Postdoctoral Research Fellow in 2025 at Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney (UTS). She received her Ph.D. in Software Engineering from the University of Technology Sydney (UTS) in 2024, and her Ph.D. in Management Science and Engineering from Shanghai University (SHU) in 2023. Her research interests include concept drift adaptation, data stream mining, machine learning, and information management. She have publish papers on journals and conferences like TCYB, Neurocomputing, Physica A, AJCAI, FLIN-ISKE, KES. She also serves as a reviewer of journals and conferences like TNNLS, TFS, TCYB, KBS, Neurocomputing, Neural Networks, FLINS-ISKE, AJCAI, IJCNN, AAAI, ACMM, SIGKDD Workshop, ICLR, ICML. She also works as a PC member of FLINS-ISKE 2024, AJCAI 2023.

Research Interests:

Data stream learning, Concept drift detection and adaptation, Spatio-temporal data mining, Machine learning, Ensemble learning

Recent News:

• 2025–We have a special session “Advances in Spatio-temporal Data Mining: Methods and Applications” in the ISKE 2025 conference. Congratulations!

• 2025–We have a paper accepted by Computers & Industrial Engineering. Congratulations!

• 2024–We have a special session “Autonomous Learning in Uncertain Decision Situations” in FLINS-ISKE 2024, and two papers have been accepted. Congratulations!

• 2024–We have a paper accepted by TCYB. Congratulations!

• 2024–We have a paper accepted by Physica A: Statistical Mechanics and its Applications. Congratulations!

• 2024–We have a paper accepted by Neurocomputing. Congratulations!

• 2023–We have a paper accepted by AJCAI 2023. Congratulations!

• 2023–We have a paper accepted by KES 2023. Congratulations!

• 2023–We have attended the workshop of IJCNN 2023. Congratulations!

Recent Journal Papers:

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, “TS-DM: A time segmentation-based data stream learning method for concept drift adaptation”, IEEE Transactions on Cybernetics, 2024. [Link]

• Kun Wang,Li Xiong, Rudan Xue, “Real-time data stream learning for emergency decision-making under uncertainty”, Physica A: Statistical Mechanics and its Applications, 2024. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, “Evolving gradient boost: A pruning scheme based on loss improvement ratio for learning under concept drift”, IEEE Transactions on Cybernetics, 2023. [Link]

• Kun Wang, Li Xiong, Anjin Liu, Guangquan Zhang, Jie Lu, “A self-adaptive ensemble for user interest drift learning”, Neurocomputing, 2023. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Yiliao Song, Guangquan Zhang, Li Xiong, “Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation”, Neurocomputing, 2022. [Link]

Recent Conference Papers:

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, An adaptive stacking method for multiple data streams learning under concept drift, FLINS-ISKE 2024. [Link]

• Bin Zhang, Jie Lu, Kun Wang, Guangquan Zhang, ML4MDS: A machine learning platform for multiple data stream, FLINS-ISKE 2024. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, “TCR-M: A topic change recognition-based method for data stream learning”, KES 2023. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, “An augmented learning approach for multiple data streams under concept drift”, AJCAI 2023. [Link]

• Kun Wang, Anjin Liu, Jie Lu, Guangquan Zhang, Li Xiong, “An elastic gradient boosting decision tree for concept drift learning”, AJCAI 2020. [Link]

Research Services:

• PC member/Organizer

  ISKE 2025, FLINS-ISKE 2024, AJCAI 2023

• Journal reviewer

  TNNLS, TFS, TCYB, KBS, Neurocomputing, Neural Networks, The Journal of Supercomputing

• Conference reviewer

  FLINS-ISKE, AJCAI, IJCNN, AAAI, ACMM, SIGKDD Workshop, ICLR, ICML

Activities:

• 2024–FLINS-ISKE 2024 (Oral presentation)

• 2023–AJCAI 2023 (Oral presentation)

• 2023–KES 2023 (Oral presentation)

• 2023–IJCNN 2023 (Attend workshop)

• 2020–AJCAI 2020 (Oral presentation)

• 2019–LISS 2018 (Oral presentation)

• 2017–CNAIS 2017(Oral presentation)