Staff Profile
Dr Shidong Wang
Research Associate
Background
I am a Research Associate at NEOLab, School of Engineering, Newcastle University, where I am responsible for developing cutting-edge deep learning algorithms for the PYRAMID and Deep Lakes projects. Before joining NEOLab, I worked as a Research Assistant in the same department on the WeACT project. I have also worked as a Research Assistant at Open Lab, School of Computing, Newcastle University.
Publications
Qualifications
PhD in Artificial Intelligence (University of East Anglia; 2021);
First Class BSc (Hons) in Applied Computing (2014).
Memberships
Member of IEEE
Research Interests
My research interests span a broad range of topics in Artificial Intelligence (AI) and Computer Vision, all unified by the vision of addressing critical challenges in seeking interpretable numerical solutions for complex modelling. My particular focus is on developing innovative machine learning and deep learning approaches that can significantly improve modelling accuracy and efficiency. Ultimately, I hope these advances will empower individuals and stakeholders to better leverage and derive value from the data.
Research Funding
GCRF and Newton Fund Consolidation Account (GNCA): Deep Lakes (CoI; £20,921; 10/22 - 03/23);
Guest Lecturer for CSC8637 Deep Learning, School of Computing, Newcastle University
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Articles
- Zhao X, Shen Y, Wang S, Zhang H. Generating diverse augmented attributes for generalized zero shot learning. Pattern Recognition Letters 2023, 166, 126-133.
- Wang S, Peppa MV, Xiao W, Maharjan SB, Joshi SP, Mills JP. A second-order attention network for glacial lake segmentation from remotely sensed imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2022, 189, 289-301.
- Wang S, Ren Y, Parr G, Guan Y, Shao L. Invariant Deep Compressible Covariance Pooling for Aerial Scene Categorization. IEEE Transactions on Geoscience and Remote Sensing 2021, 59(8), 6549-6561.
- Wang S, Guan Y, Shao L. Multi-Granularity Canonical Appearance Pooling for Remote Sensing Scene Classification. IEEE Transactions on Image Processing 2020, 29, 5396-5407.