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

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

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

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

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

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

Sun, Y, Tang, K, Minku, LL, Wang, S & Yao, X 2016, 'Online Ensemble Learning of Data Streams with Gradually Evolved Classes', IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1532-1545. https://doi.org/10.1109/TKDE.2016.2526675

Guo, Y, Jiao, L, Wang, S, Wang, S, Liu, F, Rong, K & Xiong, T 2015, 'A novel dynamic rough subspace based selective ensemble', Pattern Recognition, vol. 48, no. 5, pp. 1638-1652. https://doi.org/10.1016/j.patcog.2014.11.001

Wang, S, Minku, LL & Yao, X 2015, 'Resampling-based ensemble methods for online class imbalance learning', IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 5, pp. 1356 - 1368. https://doi.org/10.1109/TKDE.2014.2345380

Wang, S, Minku, L & Yao, X 2013, 'Online Class Imbalance Learning and Its Applications in Fault Detection', International Journal of Computational Intelligence and Applications, vol. 12, no. 4, 1340001. https://doi.org/10.1142/S1469026813400014

Wang, S & Yao, X 2011, 'Relationships Between Diversity of Classification Ensembles and Single-Class Performance Measures', IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2011.207

Conference contribution

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.

Wang, S, Minku, LL & Yao, X 2014, A multi-objective ensemble method for online class imbalance learning. in Proceedings of the International Joint Conference on Neural Networks., 6889545, Institute of Electrical and Electronics Engineers (IEEE), pp. 3311-3318, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/IJCNN.2014.6889545

Paper

Wang, S, Minku, L, Ghezzi, D, Caltabiano, D, Tino, P & Yao, X 2013, 'Concept Drift Detection for Online Class Imbalance Learning' Paper presented at Proceedings of the 2013 International Joint Conference on Neural Networks , United Kingdom, 4/08/13, . https://doi.org/10.1109/IJCNN.2013.6706768

Wang, S, Minku, L & Yao, X 2013, 'A Learning Framework for Online Class Imbalance Learning' Paper presented at IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), 2013 , Singapore, United Kingdom, 16/04/13 - 19/04/13, pp. 36-45. https://doi.org/10.1109/CIEL.2013.6613138

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