Professor Jiaxiang Zhang

Professor in Artificial Intelligence

Computer Science

jiaxiang.zhang@swansea.ac.uk

Office - 343
Third Floor
Computational Foundry
Bay Campus

Available For Postgraduate Supervision

Research Links

https://orcid.org/0000-0002-4758-0394

ResearchGate

Google Scholar

ResearcherID

About

Jiaxiang Zhang is a Professor of Artificial Intelligence at the Department of Computer Science. His research focuses on human and machine intelligence. He combines computational modelling, machine learning, brain imaging, and experimental approaches to understand human cognition, ageing, and cognitive deficits in neurological and psychiatric disorders. On machine intelligence, he is interested in developing artificial agents to mimic the human capacity of complex cognitive operation and social interaction.

Professor Zhang received his PhD in Computational Neuroscience from the University of Bristol. He was a research fellow at the University of Birmingham (2008-10) and an investigator scientist at the MRC Cognition and Brain Sciences Unit, University of Cambridge (2010-14). From 2015-22, he was a faculty member at Cardiff University (Lecturer 2015-17, Senior Lecturer 2017-21, Reader 2021-22), where he set up the Cognition and Computational Brain Lab. He is the PI or Co-I of research grants from the ERC, MRC, BBSRC and the Wellcome Trust.

Areas Of Expertise

Artificial intelligence
Machine learning
Cognitive neuroscience
Brain imaging
Neuroinformatics
Human decision-making
Big-data analytics
Digital health

Publications

Research Highlights

Bundil, I., Baltruschat, S., & Zhang, J. (2024). Characterising and differentiating cognitive and motor speed in older adults: structural equation modelling on a UK longitudinal birth cohort. BMJ Open, 14(8), e083968
https://doi.org/10.1136/bmjopen-2024-083968, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67442
Aslam, S., Chen, Q., & Zhang, J. (2024). DELRecon: Depth Electrode Reconstruction Toolbox for Stereo-EEG. In Lecture Notes in Computer Science (pp. 135-148). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_10, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67433
Morgan, M., Rahat, A., Jenkins, G., & Zhang, J. (2024). Explainable Machine Learning: Predicting Clinical Outcomes in Welsh Emergency Departments. In Lecture Notes in Computer Science (pp. 290-301). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_21, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67432
Ozkan, A. & Zhang, J. (2024). Information sources and congruency modulate preference-based decision-making processes. Journal of Cognitive Psychology, 36(6), 775-792.
https://doi.org/10.1080/20445911.2024.2384666, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67175
Tomassini, A., Cope, T., Zhang, J., & Rowe, J. (2024). Parkinson’s disease impairs cortical sensori-motor decision-making cascades. Brain Communications, 6(2)
https://doi.org/10.1093/braincomms/fcae065, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66058
Read, M., Berry, S., Graham, K., Voets, N., Zhang, J., Aggleton, J., Lawrence, A., & Hodgetts, C. (2024). Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T. Neuropsychologia, 194, 108783
https://doi.org/10.1016/j.neuropsychologia.2023.108783, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65434
Lopes, M., Hamandi, K., Zhang, J., & Creaser, J. (2023). The role of additive and diffusive coupling on the dynamics of neural populations. Scientific Reports, 13(1), 4115
https://doi.org/10.1038/s41598-023-30172-3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62929
http://dx.doi.org/10.1038/s41598-023-30172-3
Karahan, E., Tait, L., Si, R., Özkan, A., Szul, M., Graham, K., Lawrence, A., & Zhang, J. (2022). The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure. Communications Biology, 5(1)
https://doi.org/10.1038/s42003-022-03974-w, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61343
Tait, L. & Zhang, J. (2022). +microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG. NeuroImage, 258, 119346
https://doi.org/10.1016/j.neuroimage.2022.119346, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61201
Krzemiński, D. & Zhang, J. (2022). Imperfect integration: Congruency between multiple sensory sources modulates decision-making processes. Attention, Perception, & Psychophysics, 84(5), 1566-1582.
https://doi.org/10.3758/s13414-021-02434-7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61202

All Research

Journal Articles

Bundil, I., Baltruschat, S., & Zhang, J. (2024). Characterising and differentiating cognitive and motor speed in older adults: structural equation modelling on a UK longitudinal birth cohort. BMJ Open, 14(8), e083968
https://doi.org/10.1136/bmjopen-2024-083968, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67442
Ozkan, A. & Zhang, J. (2024). Information sources and congruency modulate preference-based decision-making processes. Journal of Cognitive Psychology, 36(6), 775-792.
https://doi.org/10.1080/20445911.2024.2384666, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67175
Tomassini, A., Cope, T., Zhang, J., & Rowe, J. (2024). Parkinson’s disease impairs cortical sensori-motor decision-making cascades. Brain Communications, 6(2)
https://doi.org/10.1093/braincomms/fcae065, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66058
Read, M., Berry, S., Graham, K., Voets, N., Zhang, J., Aggleton, J., Lawrence, A., & Hodgetts, C. (2024). Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T. Neuropsychologia, 194, 108783
https://doi.org/10.1016/j.neuropsychologia.2023.108783, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65434
Lopes, M., Hamandi, K., Zhang, J., & Creaser, J. (2023). The role of additive and diffusive coupling on the dynamics of neural populations. Scientific Reports, 13(1), 4115
https://doi.org/10.1038/s41598-023-30172-3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62929
http://dx.doi.org/10.1038/s41598-023-30172-3
Karahan, E., Tait, L., Si, R., Özkan, A., Szul, M., Graham, K., Lawrence, A., & Zhang, J. (2022). The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure. Communications Biology, 5(1)
https://doi.org/10.1038/s42003-022-03974-w, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61343
Tait, L. & Zhang, J. (2022). +microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG. NeuroImage, 258, 119346
https://doi.org/10.1016/j.neuroimage.2022.119346, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61201
Krzemiński, D. & Zhang, J. (2022). Imperfect integration: Congruency between multiple sensory sources modulates decision-making processes. Attention, Perception, & Psychophysics, 84(5), 1566-1582.
https://doi.org/10.3758/s13414-021-02434-7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61202
Wolpe, N., Hezemans, F., Rae, C., Zhang, J., & Rowe, J. (2022). The pre-supplementary motor area achieves inhibitory control by modulating response thresholds. Cortex, 152, 98-108.
https://doi.org/10.1016/j.cortex.2022.03.018, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61348
Lopes, M., Bhatia, S., Brimble, G., Zhang, J., & Hamandi, K. (2022). A Computational Biomarker of Photosensitive Epilepsy from Interictal EEG. eneuro, 9(3), ENEURO.0486-21.2022.
https://doi.org/10.1523/eneuro.0486-21.2022, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61203
Tait, L. & Zhang, J. (2022). MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses. NeuroImage, 251, 119006
https://doi.org/10.1016/j.neuroimage.2022.119006, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61345
Tait, L., Lopes, M., Stothart, G., Baker, J., Kazanina, N., Zhang, J., & Goodfellow, M. (2021). A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease. PLOS Computational Biology, 17(8), e1009252
https://doi.org/10.1371/journal.pcbi.1009252, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61204
http://dx.doi.org/10.1371/journal.pcbi.1009252
Zajkowski, W., Krzemiński, D., Barone, J., Evans, L., & Zhang, J. (2021). Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans. Computational Brain and Behavior, 4(2), 191-212.
https://doi.org/10.1007/s42113-020-00096-6, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61251
Szul, M., Bompas, A., Sumner, P., & Zhang, J. (2020). The validity and consistency of continuous joystick response in perceptual decision-making. Behavior Research Methods, 52(2), 681-693.
https://doi.org/10.3758/s13428-019-01269-3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61327
Krzemiński, D., Masuda, N., Hamandi, K., Singh, K., Routley, B., & Zhang, J. (2020). Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy. Network Neuroscience, 4(2), 374-396.
https://doi.org/10.1162/netn_a_00125, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61206
Hodgetts, C., Stefani, M., Williams, A., Kolarik, B., Yonelinas, A., Ekstrom, A., Lawrence, A., Zhang, J., & Graham, K. (2020). The role of the fornix in human navigational learning. Cortex, 124, 97-110.
https://doi.org/10.1016/j.cortex.2019.10.017, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61205
Karahan, E., Costigan, A., Graham, K., Lawrence, A., & Zhang, J. (2019). Cognitive and White-Matter Compartment Models Reveal Selective Relations between Corticospinal Tract Microstructure and Simple Reaction Time. The Journal of Neuroscience, 39(30), 5910-5921.
https://doi.org/10.1523/jneurosci.2954-18.2019, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61207
Wolpe, N., Zhang, J., Nombela, C., Ingram, J., Wolpert, D., Cam-CAN, ., & Rowe, J. (2018). Sensory attenuation in Parkinson’s disease is related to disease severity and dopamine dose. Scientific Reports, 8(1)
https://doi.org/10.1038/s41598-018-33678-3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61344
Dima, D., Perry, G., Messaritaki, E., Zhang, J., & Singh, K. (2018). Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces. Human Brain Mapping, 39(10), 3993-4006.
https://doi.org/10.1002/hbm.24226, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61341
Mason, S., Zhang, J., Begeti, F., Guzman, N., Lazar, A., Rowe, J., Barker, R., & Hampshire, A. (2015). The role of the amygdala during emotional processing in Huntington's disease: From pre-manifest to late stage disease. Neuropsychologia, 70, 80-89.
https://doi.org/10.1016/j.neuropsychologia.2015.02.017, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61342
Zhang, J. & Rowe, J. (2014). Dissociable mechanisms of speed-accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift-diffusion model. Frontiers in Neuroscience, 8
https://doi.org/10.3389/fnins.2014.00069, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61340
Song, Y. & Zhang, J. (2013). Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction. Expert Systems with Applications, 40(14), 5477-5489.
https://doi.org/10.1016/j.eswa.2013.04.025, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61339
Zhang, J., Kriegeskorte, N., Carlin, J., & Rowe, J. (2013). Choosing the Rules: Distinct and Overlapping Frontoparietal Representations of Task Rules for Perceptual Decisions. Journal of Neuroscience, 33(29), 11852-11862.
https://doi.org/10.1523/jneurosci.5193-12.2013, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61338
Zhang, J., Hughes, L., & Rowe, J. (2012). Selection and inhibition mechanisms for human voluntary action decisions. NeuroImage, 63(1), 392-402.
https://doi.org/10.1016/j.neuroimage.2012.06.058, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61336
Song, Y., Crowcroft, J., & Zhang, J. (2012). Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. Journal of Neuroscience Methods, 210(2), 132-146.
https://doi.org/10.1016/j.jneumeth.2012.07.003, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61337
Zhang, J. (2012). The Effects of Evidence Bounds on Decision-Making: Theoretical and Empirical Developments. Frontiers in Psychology, 3
https://doi.org/10.3389/fpsyg.2012.00263, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61335
Zhang, J., Meeson, A., Welchman, A., & Kourtzi, Z. (2010). Learning Alters the Tuning of Functional Magnetic Resonance Imaging Patterns for Visual Forms. Journal of Neuroscience, 30(42), 14127-14133.
https://doi.org/10.1523/jneurosci.2204-10.2010, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61333
Zhang, J. & Kourtzi, Z. (2010). Learning-dependent plasticity with and without training in the human brain. Proceedings of the National Academy of Sciences, 107(30), 13503-13508.
https://doi.org/10.1073/pnas.1002506107, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61332
Zhang, J. & Bogacz, R. (2010). Bounded Ornstein–Uhlenbeck models for two-choice time controlled tasks. Journal of Mathematical Psychology, 54(3), 322-333.
https://doi.org/10.1016/j.jmp.2010.03.001, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61334
Zhang, J. & Bogacz, R. (2010). Optimal Decision Making on the Basis of Evidence Represented in Spike Trains. Neural Computation, 22(5), 1113-1148.
https://doi.org/10.1162/neco.2009.05-09-1025, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61331
Schwarzkopf, D., Zhang, J., & Kourtzi, Z. (2009). Flexible Learning of Natural Statistics in the Human Brain. Journal of Neurophysiology, 102(3), 1854-1867.
https://doi.org/10.1152/jn.00028.2009, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61329
Zhang, J., Bogacz, R., & Holmes, P. (2009). A comparison of bounded diffusion models for choice in time controlled tasks. Journal of Mathematical Psychology, 53(4), 231-241.
https://doi.org/10.1016/j.jmp.2009.03.001, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61330
Bogacz, R., Usher, M., Zhang, J., & McClelland, J. (2007). Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1485), 1655-1670.
https://doi.org/10.1098/rstb.2007.2059, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61328

Conference Contributions

Aslam, S., Chen, Q., & Zhang, J. (2024). DELRecon: Depth Electrode Reconstruction Toolbox for Stereo-EEG. In Lecture Notes in Computer Science (pp. 135-148). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_10, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67433
Morgan, M., Rahat, A., Jenkins, G., & Zhang, J. (2024). Explainable Machine Learning: Predicting Clinical Outcomes in Welsh Emergency Departments. In Lecture Notes in Computer Science (pp. 290-301). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_21, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67432

Supervision

Untitled (current)

PhD

Other supervisor: Dr Joe Macinnes
Adaptive and Explainable AI for Predicting Patient Presentations in NHS Emergency Departments. (current)

PhD

Other supervisor: Dr Alma Rahat
Enabling intelligent and proactive Telecare services for elderly and vulnerable people (current)

PhD

Other supervisor: Dr Muneeb Ahmad
Artificial intelligence driven biomedical systems (current)

PhD

Other supervisor: Prof Siraj Shaikh
Histopathology Image Processing within the Colorectal Cancer Diagnosis Pathway (current)

PhD

Other supervisor: Dr Mike Edwards
Federated Learning with Applications on Image Generation (current)

PhD

Other supervisor: Prof Xianghua Xie

Taught Modules

CSC315 Advanced Topics in Artificial Intelligence

This module exposes students to advanced topics in Artificial Intelligence, deepening the range and depth of knowledge of concepts and techniques in AI. Human-centred topics that bear on AI will also be addressed as well as integrations across approaches for Explainable AI. The course may vary topics from year to year from amongst the following or additions as appropriate: advanced machine learning (ML) models and algorithms, ML approaches to NLP, advanced symbolic AI and NLP, integrations of ML and symbolic approaches, and human-centredness (e.g., interpretability, transparency, trust).
CSC357 Brain-Inspired Artificial Intelligence

This module investigates developments in Psychology and Neuroscience that have led to various state-of-the-art Machine Learning techniques and how these relate to more traditional approaches in Computer Science.

Key Grants and Projects

FREE the MIND: the neurocognitive determinants of intentional decision (European Research Council, PI) 2016 - 2023

, €1,487,908
Characterizing the cohort-based individual variability of whole-brain multimodal connectome (Wellcome ISSF, PI) 2020 - 2021

, £46,835
A multi-generational multi-modal deep phenotyping approach to understand how early life sets the stage for brain and cognitive aging (Wellcome ISSF, joint lead) 2020 - 2022

, £145,144
The subiculum: a key interface between scene representation and event memory? (BBSRC, Co-I) 2021 - 2024

with Cal Hodgetts (PI), Andrew Lawrence, et al., £572,711
Characterising alternations of neural dynamics in intractable epilepsy using neurophysiologically-informed models (Wellcome Trust ISSF, PI) 2017 - 2018

, £44,431
Recurrence analysis for the characterisation and classification of epileptic patients (GW4, Co-I) 2019 - 2020

, with with Naoki Masuda (PI), et al., £5,000
Recurrence analysis for time-varying networks and its application to brain dynamics (GW4, Co-I) 2018 - 2018

, with with Naoki Masuda (PI), et al., £34,928
A new noradrenergic strategy to treat Impulsivity in Progressive Supranuclear Palsy (MRC, Co-I) 2017 - 2020

, with with James Rowe (PI), et al., £693,896
Characterising brain network differences during scene perception and memory in young adult APOE-e4 carriers: multi-modal imaging in ALSPAC (MRC, Co-I) 2017 - 2021

, with with Kim Graham (PI), et al., £1,420,574

Professor Jiaxiang Zhang

Email address

Twitter Account

LinkedIn Account

Research Links

About

Areas Of Expertise

Research Highlights

All Research

Untitled (current)

Adaptive and Explainable AI for Predicting Patient Presentations in NHS Emergency Departments. (current)

Enabling intelligent and proactive Telecare services for elderly and vulnerable people (current)

Artificial intelligence driven biomedical systems (current)

Histopathology Image Processing within the Colorectal Cancer Diagnosis Pathway (current)

Federated Learning with Applications on Image Generation (current)

CSC315 Advanced Topics in Artificial Intelligence

CSC357 Brain-Inspired Artificial Intelligence

Characterizing the cohort-based individual variability of whole-brain multimodal connectome (Wellcome ISSF, PI) 2020 - 2021

A multi-generational multi-modal deep phenotyping approach to understand how early life sets the stage for brain and cognitive aging (Wellcome ISSF, joint lead) 2020 - 2022

Characterising alternations of neural dynamics in intractable epilepsy using neurophysiologically-informed models (Wellcome Trust ISSF, PI) 2017 - 2018

Recurrence analysis for the characterisation and classification of epileptic patients (GW4, Co-I) 2019 - 2020

Recurrence analysis for time-varying networks and its application to brain dynamics (GW4, Co-I) 2018 - 2018