Projects per year
Organization profile
Organisation profile
The Centre for Mathematics and Algorithms for Data (MAD) at the University of Bath is an interdisciplinary group of researchers working at the intersection of Statistics, Machine Learning and Numerical Analysis with a huge range of application areas. Data Scientists use mathematics and computation to extract useful information from data. MAD aims to facilitate dialogue between groups, especially theorists in computer science and mathematics, and develop rigour in the field.
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. Our work contributes towards the following SDG(s):
Fingerprint
Dive into the research topics where Centre for Mathematics and Algorithms for Data (MAD) is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Profiles
-
Jonathan Bartlett
- Department of Mathematical Sciences - Reader, Visiting Professor in Statistics
- Centre for Mathematics and Algorithms for Data (MAD)
Person: Research & Teaching, Honorary / Visiting Staff
-
Neill Campbell
- Department of Computer Science - Reader
- Centre for the Analysis of Motion, Entertainment Research & Applications
- EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Autonomous Robotics (CENTAUR)
- Centre for Mathematics and Algorithms for Data (MAD)
- Artificial Intelligence
- Visual Computing
Person: Research & Teaching
-
Shape modelling for predicting risk of early onset hip osteoarthritis
Shardlow, T., Gill, R., Kyprianou, A. & Rambojun, A.
Engineering and Physical Sciences Research Council
4/07/22 → 3/07/23
Project: Research council
-
High impact chronic pain and UK biobank: presentation, transitions and targets for intervention
van den Windt, D., Kavanagh, E., Eccleston, C., Keogh, E. & Ehrhardt, B.
1/06/22 → 2/06/25
Project: Research-related funding
-
Transdermal Diagnostics: non-invasive, affordable, continuous glucose monitoring for diabetes management and prevention
2/05/22 → 1/05/23
Project: Research council
Research output
-
Accelerating Variance-Reduced Stochastic Gradient Methods
Driggs, D., Ehrhardt, M. J. & Schönlieb, C-B., 1 Feb 2022, In: Mathematical Programming. 191, 2, p. 671–715 45 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (SciVal)22 Downloads (Pure) -
Acoustic long-range monitoring of tropical cyclones at low frequencies
Audone, G., Blondel, P., Nunes, M., Budd, C., Harris, P. & Robinson, S., 19 Jun 2022, p. 39.Research output: Contribution to conference › Abstract › peer-review
-
Antimicrobial-associated organ injury among the elderly: A systematic review and meta-analysis protocol
Chinzowu, T., Roy, S. & Nishtala, P., 11 Feb 2022, In: BMJ Open. 12, 2, e055210.Research output: Contribution to journal › Article › peer-review
Open Access
Datasets
-
Dataset for "Machine learning outperforms clinical experts in classification of hip fractures"
Gill, R. (Creator), Ehrhardt, B. (Creator) & Murphy, E. (Creator), University of Bath, 8 Feb 2022
DOI: 10.15125/BATH-01011
Dataset
-
Dataset for "Comparison of the within-reader and inter-vendor agreement of left ventricular circumferential strains and volume indices derived from cardiovascular magnetic resonance imaging"
Cookson, A. (Creator), Odunmbaku-Mansell, D. (Creator), Fraser, K. (Creator), Gill, R. (Creator), Raimondo, A. (Creator), Domenico, B. (Creator), Frank, E. (Contributor), Kelly, N. (Contributor), Agostinho Hernandez, B. (Contributor), Fletcher, J. (Contributor), Bartlett, J. (Contributor), Sammut, E. (Contributor), Johnson, T. (Contributor) & Chiribiri, A. (Contributor), University of Bath, 15 Dec 2020
DOI: 10.15125/BATH-00890
Dataset
-
Dataset for "Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running"
Chen, X. (Creator), Weir, G. (Creator), Cazzola, D. (Creator), Trewartha, G. (Creator), Hamill, J. (Creator), Preatoni, E. (Creator) & Rodriguez Rivadulla, A. (Creator), University of Bath, 26 Jul 2021
DOI: 10.15125/BATH-00965, https://github.com/adrianrivadulla/FootNet
Dataset