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Professor Keming Yu
Professor
Tower A 025
- Email: keming.yu@brunel.ac.uk
- Tel: +44 (0)1895 266128
Research area(s)
Based on mathematical theory and data analysis methods, my research aims to explore statistical methods, models and optimal algorithms to deal with challenges in:
- New regression models and methods, including quantile regression, for Financial Econometrics and Business.
- Robust algorithms for Machine Learnign and Deep Learning.
- Statistical analysis and Machine learning for modelling loneliness and social isolation in Gerontology.
- New distributional/regression methods for the analysis of Wellbeing, health and biomedical scoences, such as obesity.
- Statistics/Machine learning methods for risk assessment in engineering, such as rail truck failure, cable fault, pipeline corrosion and wind turbine.
- Statistical theory, method, including Bayesian analysis, for the analysis of big data and small data.
Research grants and projects
Research Projects
Grants
Funder: London Mathematical Society
Duration: February 2023 - March 2003
Research in pair
Funder: Economic & Social Research Council
Duration: September 2022 - September 2025
Funder: Economic and Social Research Council
Duration: September 2022 - August 2005
Project details
Current Grants as PI:
The UK Office for National Statistics (ONS) awarded funds in its first ever open call for research in economic measurement to him for a project entitled New regression models for the analysis of wellbeing and income distribution (https://www.brunel.ac.uk/research/Projects/New-regression-models-for-the-analysis-of-well-being-and-income-distribution) (04/2019---03/2020), £5000.00.
EPSRC Case Studentship: Data Analytics for Risk Based Decision Making in Asset Integrity Management (01/01/2018—30/09/2020), £80,670.
National Institute for Health Research (NIHR): NIHR Research Methods Opportunity Funding Scheme £70,517 (2010—2011) and £68,883 (2014-2015).
NSIRC scholarship funded by TWI (Cambridge) for supporting three PhD study on Statistical analysis and modelling of corrosion data: £98,890 (2014—2020).
Research links
Co-author network
Similar research interests
Research group(s)
- IEHS
- BIAS