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

Shen, X, Pan, H, Ge, Z, Chen, W, Song, L & Wang, S 2023, 'Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization', Complex System Modeling and Simulation, vol. 3, no. 3, pp. 202-219. https://doi.org/10.23919/CSMS.2023.0008

Jiang, X, Wang, S, Liu, W & Yang, Y 2023, 'Prediction of Traditional Chinese Medicine Prescriptions Based on Multi-label Resampling', Journal of Electronic Business & Digital Economics. https://doi.org/10.1108/JEBDE-04-2023-0009

Han, L, Wang, H & Wang, S 2022, 'A Surrogate-Assisted Evolutionary Algorithm for Space Component Thermal Layout Optimization', Space: Science and Technology (United States), vol. 2022, 9856362. https://doi.org/10.34133/2022/9856362

He, Y, Wang, Y, Wang, S & Yao, X 2022, 'A cooperative ensemble method for multistep wind speed probabilistic forecasting', Chaos, Solitons and Fractals, vol. 162, 112416. https://doi.org/10.1016/j.chaos.2022.112416

He, Y, Cao, C, Wang, S & Fu, H 2022, 'Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems', Applied Energy, vol. 322, 119507. https://doi.org/10.1016/j.apenergy.2022.119507

Wang, X, Wang, H, Wang, S, Liu, Y, Yu, W, Wang, J, Xu, Q & Li, X 2022, 'Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model', Remote Sensing of the Environment, vol. 272, 112940. https://doi.org/10.1016/j.rse.2022.112940

Guo, Y, Jiao, L, Qu, R, Sun, Z, Wang, S, Wang, S & Liu, F 2021, 'Adaptive fuzzy learning superpixels representation for PolSAR image classification', IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3128908

He, Y, Li, H, Wang, S & Yao, X 2021, 'Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression', Neurocomputing, vol. 430, pp. 121-137. https://doi.org/10.1016/j.neucom.2020.10.093

Conference contribution

Yang, G, Chen, X, Zhang, T, Wang, S & Yang, Y 2023, 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, 23rd IEEE International Conference on Data Mining, Shanghai, China, 1/12/23.

Wang, Z & Wang, S 2023, Online automated machine learning for class imbalanced data streams. in 2023 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8, International Joint Conference on Neural Networks, Queensland, Australia, 18/06/23. https://doi.org/10.1109/IJCNN54540.2023.10191926

Xiao, C & Wang, S 2023, Triplets Oversampling for Class Imbalanced Federated Datasets. in D Koutra, C Plant, MG Rodriguez, E Baralis & F Bonchi (eds), Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II. 1 edn, Lecture Notes in Computer Science, vol. 14170, Springer, pp. 368–383, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Turin, Italy, 18/09/23.

Moniz, N, Branco, P, Torgo, L, Japkowicz, N, Wozniak, M & Wang, S 2022, 4th Workshop on Learning with Imbalanced Domains - Theory and Applications: Preface. in N Moniz, P Branco, L Torgo, N Japkowicz, M Wozniak & S Wang (eds), Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, 23 September 2022, ECML-PKDD, Grenoble, France. Proceedings of Machine Learning Research, vol. 183, Proceedings of Machine Learning Research, pp. 1-7, 4th International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA 2022, Grenoble, France, 23/09/22. <https://proceedings.mlr.press/v183/moniz22a.html>

Zhang, X, Wang, H, Wang, S, Liu, Y, Yu, W & Li, X 2022, A Machine-learning-based Model to Inverse Internal Solitary Wave Amplitude from Satellite Image. in 2022 Photonics & Electromagnetics Research Symposium (PIERS). Progress in Electromagnetics Research Symposium, Institute of Electrical and Electronics Engineers (IEEE), pp. 269-272, 2022 Photonics and Electromagnetics Research Symposium, PIERS 2022, Hangzhou, China, 25/04/22. https://doi.org/10.1109/PIERS55526.2022.9792885

Xiao, C & Wang, S 2022, An experimental study of class imbalance in federated learning. in 2021 IEEE Symposium Series on Computational Intelligence (SSCI)., 9660072, IEEE Symposium Series on Computational Intelligence, Institute of Electrical and Electronics Engineers (IEEE), IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), Orlando, Florida, United States, 5/12/21. https://doi.org/10.1109/SSCI50451.2021.9660072

Moniz, N, Branco, P, Torgo, L, Japkowicz, N, Woźniak, M & Wang, S 2021, 3rd Workshop on Learning with Imbalanced Domains: Preface. in N Moniz, P Branco, L Torgo, N Japkowicz, M Woźniak & S Wang (eds), Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, 17 September 2021, ECML-PKDD, Bilbao (Basque Country, Spain). Proceedings of Machine Learning Research, vol. 154, Proceedings of Machine Learning Research, pp. 1-6, 3rd International Workshop on Learning with Imbalanced Domains: Theoryand Applications, LIDTA 2021, Virtual, Online, Spain, 17/09/21. <https://proceedings.mlr.press/v154/moniz21a.html>

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