Projects per year
Personal profile
Research interests
Research interests: Bayesian inference and modelling, machine learning/deep learning, statistical signal processing, Monte Carlo numerical methods, sampling technqiues.
Please see my personal homepage for details.
Teaching interests
CM10310 Artificial Intelligence
CM50264 Machine Learning 1
CM50268 Bayesian Machine Learning
Education/Academic qualification
Bayesian Inference & Statistical Signal Processing, Doctor of Philosophy, Ph.D. , University of Cambridge
External positions
Associate Editor, IET Journals - The Institution of Engineering and Technology
Visiting Research Scientist, University of Cambridge
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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A Novel Contour-based Machine Learning Tool for Reliable Brain Tumour Resection
Chen, X. (PI)
Engineering and Physical Sciences Research Council
1/05/24 → 30/04/26
Project: Research council
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Severe Storm Wave Loads on Offshore Wind Turbine Foundations (SEASWALLOWS)
Zang, J. (PI), Chen, X. (CoI) & Reale, C. (CoI)
Engineering and Physical Sciences Research Council
1/10/21 → 30/09/25
Project: Research council
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An artificial intelligence disease monitoring tool for patients with brain tumours
Price, S. J. (PI), Schönlieb, C. B. (CoPI), Chen, X. (CoI), Li, C. (CoI), Matys, T. (CoI), Das, T. (CoI) & Jenkinson, M. (CoI)
1/02/22 → 31/01/23
Project: Central government, health and local authorities
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An artificial intelligence disease monitoring tool for patients with brain tumours
Chen, X. (PI)
National Institute for Health Research
1/09/21 → 31/08/22
Project: Central government, health and local authorities
Research output
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A new Gaussian Process based model for non-linear wave loading on vertical cylinders
Tang, T., Ryan, G., Ding, H., Chen, X., Zang, J., Taylor, P. H. & Adcock, T. A. A., 31 Mar 2024, In: Coastal Engineering. 188, 104427.Research output: Contribution to journal › Article › peer-review
Open AccessFile9 Citations (SciVal)133 Downloads (Pure) -
Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy
Rivadulla, A. R., Chen, X., Cazzola, D., Trewartha, G. & Preatoni, E., 11 Jul 2024, (E-pub ahead of print) In: Sports Biomechanics.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (SciVal)111 Downloads (Pure) -
Consumer-priced wearable sensors combined with deep learning can be used to accurately predict ground reaction forces during various treadmill running conditions
Carter, J., Chen, X., Cazzola, D., Trewartha, G. & Preatoni, E., 29 Aug 2024, In: PeerJ. 12, 8, e17896.Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Citations (SciVal)36 Downloads (Pure) -
Data Informed Model Test Design With Machine Learning – An Example in Nonlinear Wave Load on a Vertical Cylinder
Tang, T., Ding, H., Dai, S., Chen, X., Taylor, P. H., Zang, J. & Adcock, T. A. A., 1 Apr 2024, In: Journal of Offshore Mechanics and Arctic Engineering. 146, 2, 9 p., 021204 .Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Citations (SciVal)34 Downloads (Pure) -
Data should be made as simple as possible but not simpler: the method chosen for dimensionality reduction and its parameters can affect the clustering of runners based on their kinematics
Rivadulla, A., Chen, X., Cazzola, D., Trewartha, G. & Preatoni, E., 31 Dec 2024, In: Journal of Biomechanics. 177, 112433.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (SciVal)17 Downloads (Pure)
Datasets
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Dataset for "Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running"
Rodriguez Rivadulla, A. (Creator), Chen, X. (Creator), Weir, G. (Creator), Cazzola, D. (Creator), Trewartha, G. (Creator), Hamill, J. (Creator) & Preatoni, E. (Creator), University of Bath, 26 Jul 2021
DOI: 10.15125/BATH-00965, https://github.com/adrianrivadulla/FootNet
Dataset
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Full Body Kinematics and Ground Reaction Forces of Fifty Heterogeneous Runners Completing Treadmill Running at Various Speeds and Gradients
Carter, J. (Creator), Chen, X. (Creator), Cazzola, D. (Creator), Trewartha, G. (Creator) & Preatoni, E. (Creator), University of Bath, 30 May 2024
DOI: 10.15125/BATH-01341
Dataset