Dr Shuo Wang PhD

Dr Shuo Wang

School of Computer Science
Lecturer of Computer Science

Contact details

Address
School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Shuo Wang is a lecturer at 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, 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

He, Y, Qin, Y, Wang, S, Wang, X & Wang, C 2019, 'Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network', Applied Energy, vol. 233-234, pp. 565-575. https://doi.org/10.1016/j.apenergy.2018.10.061

Zhang, H, Liu, W, Wang, S, Shan, J & Liu, Q 2019, 'Resample-based ensemble framework for drifting imbalanced data streams', IEEE Access, vol. 7, pp. 65103-65115. https://doi.org/10.1109/ACCESS.2019.2914725

Wang, S, Minku, LL & Yao, X 2018, 'A Systematic Study of Online Class Imbalance Learning With Concept Drift', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4802-4821. https://doi.org/10.1109/TNNLS.2017.2771290

Guo, Y, Jiao, L, Wang, S, Wang, S & Liu, F 2018, 'Fuzzy sparse autoencoder framework for single image per person face recognition', IEEE Transactions on Cybernetics, vol. 48, no. 8, pp. 2402-2415. https://doi.org/10.1109/TCYB.2017.2739338

Guo, Y, Jiao, L, Wang, S, Wang, S, Liu, F & Hua, W 2018, 'Fuzzy superpixels for polarimetric SAR images classification', IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp. 2846-2860. https://doi.org/10.1109/TFUZZ.2018.2814591

He, Y, Liu, R, Li, H, Wang, S & Lu, X 2017, 'Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory', Applied Energy, vol. 185, pp. 254-266. https://doi.org/10.1016/j.apenergy.2016.10.079

Conference contribution

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, IEEE Computer Society Press, IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), 4/12/21. https://doi.org/10.1109/SSCI50451.2021.9660072

Wang, S & Minku, L 2020, AUC estimation and concept drift detection for imbalanced data streams with multiple classes. in Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020., 9207377, Proceedings of International Joint Conference on Neural Networks, IEEE Computer Society Press, IEEE International Joint Conference on Neural Networks (IJCNN), 2020 , Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207377

Li, K, Xiang, Z, Chen, T, Wang, S & Tan, KC 2019, Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical Study. in 42nd International Conference on Software Engineering (ICSE 2020). Association for Computing Machinery (ACM), 42nd International Conference on Software Engineering (ICSE 2020), Seoul, Korea, Republic of, 23/05/20. https://doi.org/10.1145/3377811.3380360

WANG, S, MINKU, LL & YAO, X 2016, Dealing with Multiple Classes in Online Class Imbalance Learning. in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). AAAI Press, New York City, pp. 2118-2124. <https://www.ijcai.org/Abstract/16/302>

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