Home / Directory / Professor Richard Samworth

Professor Richard Samworth

Professor of Statistics, Statistical Laboratory, University of Cambridge
Teaching Fellow, St John's College

Contact information

01223 337950

Statistical Laboratory
Centre for Mathematical Sciences
Wilberforce Road
United Kingdom


I obtained my PhD in Statistics from the University of Cambridge in 2004, and after a Research Fellowship at St John's College, joined the Statistical Laboratory as a Lecturer in 2005.  I subsequently became a Reader (2010) and Professor of Statistics (2013), and remain a Fellow of St John's.

Research interests

My main research interests are in nonparametric and high-dimensional statistics. Particular topics include shape-constrained density and other nonparametric function estimation problems, nonparametric classification, clustering and regression, Independent Component Analysis, the bootstrap and high-dimensional variable selection problems.


Shah, R. D. and Samworth, R. J. (2013) Variable selection with error control: Another look at Stability Selection, J. Roy. Statist. Soc., Ser. B, 75, 55-80.
Samworth, R. J. and Yuan, M. (2012) Independent component analysis via nonparametric maximum likelihood estimation. Ann. Statist., 40, 2973-3002.
Samworth, R. J. (2012) Optimal weighted nearest neighbour classifiers, Ann. Statist., 40, 2733-2763.
Dümbgen, L., Samworth, R. and Schuhmacher, D. (2011) Approximation by log-concave distributions with applications to regression, Ann. Statist., 39, 702-730.
Cule, M., Samworth, R. and Stewart, M. (2010) Maximum likelihood estimation of a multi-dimensional log-concave density, J. Roy. Statist. Soc., Ser. B. (with discussion), 72, 545-607.

About us

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

  • Supports and connects the growing data science and AI research community 
  • Builds research capacity in data science and AI to tackle complex issues 
  • Drives new research challenges through collaborative research projects 
  • Promotes and provides opportunities for knowledge transfer 
  • Identifies and provides training courses for students, academics, industry and the third sector 
  • Serves as a gateway for external organisations 

Join us