Kun Wang is currently a 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 spatio-temporal data mining, concept drift adaptation, data stream mining, machine learning, and information management. She has published papers on journals and conferences like IEEE TCYB, IEEE TKDE, Neurocomputing, Physica A, AJCAI, FLIN-ISKE, KES, AAAI. She also serves as a reviewer of journals and conferences like IEEE TNNLS, IEEE TFS, IEEE TCYB, IEEE IOT, IEEE SMC systems, KBS, Neurocomputing, Neural Networks, FLINS-ISKE, AJCAI, IJCNN, AAAI, ACMM, SIGKDD Workshop, ICLR, ICML, AISTATS. She also works as a PC member of FLINS-ISKE 2026, ISKE 2025, FLINS-ISKE 2024, AJCAI 2023.

Research Interests:

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

Recent News:

• 2026–We have a special session “Spatio-Temporal Autonomous Learning in Uncertain Decision Situations” in FLINS-ISKE 2026. Congratulations!

• 2025–We have a co-author paper accepted by “AAAI”. Congratulations!

• 2025–We have a paper accepted by “IEEE TKDE”. Congratulations!

• 2025–We have a paper accepted by ISKE 2025 conference. Congratulations!

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

• 2025–We have a co-author 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 IEEE 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, “Adaptive Information Fusion-Based Concept Drift Learning for Evolving Multiple Data Streams”, IEEE Transactions on Knowledge and Data Engineering, 2025. [Link]

• 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:

• En Yu, Jie Lu, Kun Wang, Guangquan Zhang, “Drift-aware collaborative assistance mixture of experts for heterogeneous multistream learning”, AAAI 2026. Accepted.

• Kun Wang, Hang Yu, “Adaptive Diffusion Learning for Non-Stationary Data Streams with Concept Drift”, ISKE 2025. Accepted.

• 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 Projects:

• 2026-2027, Shanghai Magnolia Talent Plan Pujiang Project (2025PJA041), PI

Research Services:

• PC member/Organizer

  FLINS-ISKE 2026, ISKE 2025, FLINS-ISKE 2024, AJCAI 2023

• Journal reviewer

  IEEE TNNLS, IEEE TFS, IEEE TCYB, IEEE IOT, IEEE SMC systems, KBS, Neurocomputing, Neural Networks, The Journal of Supercomputing

• Conference reviewer

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

Activities:

• 2025–ISKE 2025 (Oral presentation)

• 2025–CNCC 2025 (Oral presentation)

• 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)