Dr Shuo Wang PhD

Dr Shuo Wang

School of Computer Science
Associate Professor

Contact details

Email
s.wang.2@bham.ac.uk
View my research portal
Address
School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Shuo Wang is an Associate Professor in the School of Computer Science at the University of Birmingham. Her research interests include data stream classification, class imbalance learning and ensemble learning approaches in machine learning, and their applications in social media analysis, software engineering and fault detection.

As the leading researcher in these areas, she proposed and formulated the problems of multi-class imbalance and online class imbalance. Her work has been published in internationally renowned journals and conferences, such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems (impact factor: 7.982), IEEE Transactions on Cybernetics (impact factor: 8.803) and International Joint Conference on Artificial Intelligence (IJCAI).

Please follow the link below to find out more about Shuo's work:

Dr Shuo Wang- personal webpage.

Qualifications

  • PhD in Computer Science, University of Birmingham, UK, 2011

  • BSc in Software Engineering, Beijing University of Technology, China, 2006

  • Staff and Educational Development Association (SEDA) teaching qualification

Biography

Shuo Wang is a lecturer at School of Computer Science at University of Birmingham. Before that, she spent a year lecturing at Birmingham City University. She was a research fellow at the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA) at the University of Birmingham between 2011 and 2018. She received the Ph.D. degree in Computer Science from the University of Birmingham in 2011, sponsored by the Overseas Research Students Award (ORSAS) from the British Government.

Dr. Wang's research interests include data stream classification, class imbalance learning and ensemble learning approaches in machine learning, and their applications in social media analysis, software engineering and fault detection. Her work has been published in internationally renowned journals and conferences, such as IEEE Transactions on Knowledge and Data Engineering and International Joint Conference on Artificial Intelligence (IJCAI).

She has been a guest editor of Neurocomputing and Connection Science and the workshop organizer of IJCAI'17 and ICDM'19. A tutorial on learning from imbalanced data streams was given at WCCI'18. She had also given invited talks at UCL, Xi'dian University, Chinese Academy of Sciences (Institute of Oceanology), etc.

Teaching

  • MSc Software Engineering 

Research

  • Data stream classification
  • Class imbalance learning
  • Automated software testing

Other activities

  • Chair the Workshop on Learning in the Presence of Class Imbalance and Concept Drift, in conjunction with International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017.
  • Guest editor of the Special Issue "Learning in the Presence of Class Imbalance and Concept Drift" at journal Neurocomputing.
  • Guest editor of the Special Issue "Learning from Data Streams and Class Imbalance" at journal Connection Science.
  • Tutorial on Learning Class Imbalanced Data Streams, IEEE World Congress on Computational Intelligence (WCCI), Rio de Janeiro, Brazil, 2018.
  • Regular Reviewer of IEEE Transactions on Knowledge and Data Engineering (TKDE) and IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
  • Take part in EU H2020 ITN-EID project ECOLE.

Publications

Recent publications

Article

He, Y, Zhu, J & Wang, S 2024, 'A Novel Neural Network-based Multi-objective Evolution Lower Upper Bound Estimation Method for Electricity Load Interval Forecast', IEEE Transactions on Systems, Man and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2024.3352665

He, Y, Zhou, J, Cao, C, Wang, S & Fu, H 2024, 'Detection of electricity theft based on Minimal Gated Memory network combined adaptive synthesis sampling and decision tree', Sustainable Energy, Grids and Networks, vol. 39, 101415. https://doi.org/10.1016/j.segan.2024.101415

Guo, Y, Zhang, W, Gao, Y, Jiao, L, Wang, S, Du, J & Liu, F 2024, 'Revisiting non-learned operators based deep learning for image classification: a lightweight directional-aware network', Artificial Intelligence Review, vol. 58, 40. https://doi.org/10.1007/s10462-024-11038-0

Skanupong, N, Xu, Y, Yu, L, Wan, Z & Wang, S 2024, 'The Convolutional Neural Network for Pacific Decadal Oscillation Forecast', Environmental Research Letters. https://doi.org/10.1088/1748-9326/ad8be2

Conference contribution

Yang, L, Zhou, S, Cheng, J, Zhang, F, Wan, J, Wang, S & Lee, M 2025, DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation. in O Rambow, L Wanner, M Apidianaki, H Al-Khalifa, B Di Eugenio & S Schockaert (eds), Proceedings of the 31st International Conference on Computational Linguistics. International conference on computational linguistics, Association for Computational Linguistics, ACL, pp. 5890–5901, The 31st International Conference on Computational Linguistics, Abu Dhabi, United Arab Emirates, 19/01/25. <https://aclanthology.org/2025.coling-main.393/>

Yang, G, Chen, X, Zhang, T & Wang, S 2024, A Multi-Model Approach for Handling Concept Drifting Data in Federated Learning. in 2024 20th International Conference on Mobility, Sensing and Networking (MSN). International Conference on Mobile Ad-hoc and Sensor Networks, MSN, IEEE, The 20th International Conference on Mobility, Sensing and Networking , Harbin, China, 20/12/24.

Yang, G, Chen, X, Zhang, T, Wang, S & Yang, Y 2024, An Impact Study of Concept Drift in Federated Learning. in 2023 IEEE International Conference on Data Mining (ICDM). IEEE International Conference on Data Mining (ICDM), IEEE, pp. 1457-1462, 23rd IEEE International Conference on Data Mining, Shanghai, China, 1/12/23. https://doi.org/10.1109/ICDM58522.2023.00191

Chen, X, Wang, S, Zhang, T & Yang, G 2024, A Study of Virtual Concept Drift in Federated Data Stream Learning. in 2024 20th International Conference on Mobility, Sensing and Networking (MSN). International Conference on Mobile Ad-hoc and Sensor Networks, MSN, IEEE, The 20th International Conference on Mobility, Sensing and Networking , Harbin, China, 20/12/24.

Xiao, C, Zuo, Z & Wang, S 2024, FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation. in The 1st Workshop on Federated Learning for Unbounded and Intelligent Decentralization (FLUID) in conjunction with AAAI 2025. Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, The 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, Pennsylvania, United States, 27/02/25. https://doi.org/10.48550/arXiv.2412.16582

Groom, S, Morris, D, Anderson, L & Wang, S 2024, Modeling Defensive Dynamics in Football: A Hidden Markov Model-Based Approach for Man-Marking and Zonal Defending Corner Analysis. in The 2nd International Workshop on Intelligent Technologies for Precision Sports Science (IT4PSS) in Conjunction with the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24). The 2nd International Workshop on Intelligent Technologies for Precision Sports Science (IT4PSS) , Jeju Island, Korea, Republic of, 4/08/24.

Lyu, H, Herring, D, Wang, L, Ninic, J, Andrews, J, Li, M, Kocvara, M, Spill, F & Wang, S 2024, Multi-Objective Optimization for Flexible Building Space Usage. in 2024 IEEE Conference on Artificial Intelligence (CAI). Artificial Intelligence (CAI), IEEE Conference on, IEEE, pp. 932-939, 2024 IEEE Conference on Artificial Intelligence , Singapore, 25/07/24. https://doi.org/10.1109/CAI59869.2024.00172

Wang, Z & Wang, S 2024, Online Automated Imbalanced Learning via Adaptive Thompson Sampling. in A Antonacopoulos, S Chaudhuri, R Chellappa, C-L Liu, S Bhattacharya & U Pal (eds), Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part XXIV. Lecture Notes in Computer Science, vol. 15324, Springer, pp. 156-172, The 27th International Conference on Pattern Recognition, Kolkata, India, 1/12/24. https://doi.org/10.1007/978-3-031-78383-8_11

Zhang, T, Chen, X, Yang, G & Wang, S 2024, Overcoming Dynamic Class Imbalance in Federated Data Stream Learning. in The 20th International Conference on Mobility, Sensing and Networking (MSN 2024). International Conference on Mobile Ad-hoc and Sensor Networks, MSN, IEEE, The 20th International Conference on Mobility, Sensing and Networking , Harbin, China, 20/12/24.

Wang, Z & Wang, S 2024, Zero-shot Automated Class Imbalanced Learning. in A Antonacopoulos, S Chaudhuri, R Chellappa, C-L Liu, S Bhattacharya & U Pal (eds), Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part XXIV. Lecture Notes in Computer Science, vol. 15324, Springer, pp. 140-155, The 27th International Conference on Pattern Recognition, Kolkata, India, 1/12/24. https://doi.org/10.1007/978-3-031-78383-8_10

Editorial

Cheng, R, Escalante, HJ, Tu, W-W, Rijn, JNV, Wang, S & Yang, Y 2024, 'Guest Editorial: AutoML for Nonstationary Data', IEEE Transactions on Artificial Intelligence, vol. 5, no. 6, pp. 2456-2457. https://doi.org/10.1109/TAI.2024.3387583

View all publications in research portal