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Department of Computer Science and Technology

  • PhD Student

Hi,I am currently a final Year PhD student in Artificial Intelligence Group. I am supervised by Dr Pietro Lio and Dr Mateja Jamnik. My Research focus on using machine learning and neuroscience to understand reasoning, in particular diagrammatic reasoning. In these projects I have also extended explored Graph Neural Networks both as a way of capturing relations between elements in the reasoning tasks, and GNN itself. In the past I have also done a couple of projects on applying machine learning on biomedical image processing and analysis.




Duo Wang, Mateja Jamnik, Pietro Liò (2020)  Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds. Under Review


Yiren Zhao, Duo Wang, Daniel Bates, Robert Mullins, Mateja Jamnik, Pietro Lio (2020)  Learned Low Precision Graph Neural Networks. Under Review


Yiren Zhao, Duo Wang, Xitong Gao, Robert Mullins, Mateja Jamnik, Pietro Lio (2020)  Probablistic Dual Architecture Seach on Graphs. Best Student Paper Award, DLG-AAAI 2021


Colleen P.E. Rollins, Jane R. Garrison , Maite Arribas, Aida Seyedsalehi, Zhi Li, Raymond C.K. Chan , Junwei Yang , Duo Wang , Pietro Lio , Chao Yan , Zheng-hui Yi , Arnaud Cachia , Rachel Upthegrove , Bill Deakin , Jon S. Simons , Graham K. Murray, John Suckling (2020)  Evidence in cortical folding patterns for prenatal predispositions to hallucinations in schizophrenia. Accepted at Nature Translational Psychiatry


Duo Wang, Mateja Jamnik, Pietro Liò (2020)  Abstract Diagrammatic Reasoning with Multiplex Graph Networks. Accepted at ICLR 2020


Duo Wang, Mateja Jamnik, Pietro Liò (2019)  Unsupervised and Interpretable Scene Discovery with Discrete-Attend-Infer-Repeat. Accepted at ICML 2019 Self-Supervised Learning Workshop


Duo Wang, Mateja Jamnik, Pietro Liò (2019)  Unsupervised Extraction of Interpretable Graph Representations From Multiple-object Scenes. Accepted at ICML 2019 Learning and Reasoning with Graph-structured Representation Workshop


Duo Wang, Mateja Jamnik, Pietro Liò (2018)  Investigating diagrammatic reasoning with deep neural networks. Accepted at Diagrams 2018

Wang D. et al. (2017) Neural network fusion: a novel CT-MR Aortic Aneurysm image segmentation method. Accepted  at SPIE Medical Imaging conference 2018 

Jin Zhu, Duo Wang Pietro Liò (2017) A Multi-pathway 3D Dilated Convolutional Neural Network for Brain Tumor SegmentationBRATS challenge

Duo Wang, Mateja Jamnik, Pietro Liò (2017)  Modelling Neural Correlates of mathematical diagrammatic reasoning Accepted at MathPsych/ICCM 2017

Veličković P., Wang, D., Lane N., Lio', P.  (2016)  X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets. IEEE SSCI 2016 (

Hongliang Yu, Xin Wan, Guodong Liang, Duo Wang, Xiaohong Wang   (2015)  Intelligent Control System for Cement Raw Mill Quality Based on Online Analysis. 2nd International conference on advances in information processing and communication technology


Supervised Projects

Nicholas Quek, "Pairwise Relation Discriminator for unsupervised learning of Raven Progressive Matrices", MPhil 2019

Junwei Yang, “Identification and characterization of the paracingulate sulcus on T1 MRI brain scans using machine learning”, 2018 MPhil

Samuel Silvester, “Correlating Deep Neural Networks trained for visual tasks with human visual cortex activity”, 2018 Part II

Sasha Naidoo, “Deep Neural Networks for Multi-Echo fMRI Data Analysis”, 2017 MPhil

Marko Stankovic, “Deep generative modelling for text-to-image synthesis”, 2017 Part II

Rui Zhang, “Neural network fusion: a novel CT-MR Aortic Aneurysm image segmentation method”, 2016 MPhil

Sebastian Borgeaud, “Brain tumour segmentation using Convolutional Neural Networks”, 2016 Part II

Supervised Course

Part IA, Machine Learning and Real-world data

Part IB, Foundations of Data Science

Part II, Digital Signal Processing

Part II, Machine Learning and Bayesian Inference

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