Timothy I. Cannings



School of Mathematics,

University of Edinburgh,

James Clerk Maxwell Building,

Peter Guthrie Tait Road,

Edinburgh, EH9 3FD


I am a lecturer in statistics and data science at the School of Mathematics, University of Edinburgh. I completed my PhD with Prof Richard Samworth in the Statistical Laboratory at the University of Cambridge in 2015. I then worked with Prof Yingying Fan as a Postdoc at the University of Southern California.

My research is currently supported by a three-year EPSRC New Investigator Award on `New challenges in robust statistical learning' [EP/V002694/1].

Research interests

• Statistical learning: classification and clustering

• High-dimensional data: data perturbation techniques, random projections

• Robust methods: Incomplete data, semi-supervised problems, noisy data, transfer learning

• Applications in genomics and precision medicine

Preprints and Publications

Cheng, Y., Gadd, D. A., Gieger, C., Monterrubio-Gómez, K., Zhang, Y., Berta, I., Stam, M. J., Szlachetka, N., Lobzaev, E., Campbell, A., Nangle, C., Walker, R. M., Fawns-Ritchie, C., Peters, A., Rathmann, W., Porteous, D. J., Evans, K. L., McIntosh, A. M., Cannings, T. I., Waldenberger, M., Ganna, A., McCartney, D. L., Vallejos, C. A. and Marioni, R. E. (2021) DNA Methylation scores augment 10-year risk prediction of diabetes. Preprint. (.pdf). The accompanying R package MethylPipeR-UI is available on GitHub.

Reeve, H. W. J., Cannings, T. I. and Samworth, R. J. (2021) Optimal subgroup selection. Preprint. (.pdf).

Bradley, J. R. and Cannings, T. I. (2022) Data-driven design of targeted gene panels for estimating immunotherapy biomarkers. Commun. Biol., 5, 156. (.pdf) The accompanying R package ICBioMark is available from CRAN and an associated `Behind the Paper' blog post can be read here.

Cannings, T. I. and Fan, Y. (2022) The correlation-assisted missing data estimator. J. Mach. Learn. Res., 23(41), 1-49. (.pdf)

Reeve, H. W. J., Cannings, T. I. and Samworth, R. J. (2021) Adaptive transfer learning. Ann. Statist., 49, 3618-3649. (.pdf)

Cannings, T. I. (2020) Random projections: Data perturbation for classification problems. WIREs Computational Statistics, 13, DOI: 10.1002/wics.1449.

Cannings, T. I., Fan, Y. and Samworth, R. J. (2020) Classification with imperfect training labels. Biometrika, 107, 311-330. (.pdf)

Cannings, T. I., Berrett, T. B. and Samworth, R. J. (2020) Local nearest neighbour classification with applications to semi-supervised learning. Ann. Statist., 48, 1789-1814. (.pdf)

Dubourg-Felonneau, G., Cannings, T. I., Cotter, F., Thompson, H., Patel, N., Cassidy, J. W. and Clifford, H. W. (2018) A framework for implementing machine learning on Omics data. NeurIPS ML4H workshop. (.pdf)

Cannings, T. I. and Samworth, R. J. (2017) Random projection ensemble classification. J. Roy. Statist. Soc., Ser. B (with discussion), 79 , 959-1035. (.pdf) The accompanying R package RPEnsemble is available from CRAN.

Cannings, T. I. (2015) New Approaches to Modern Statistical Classification Problems. PhD thesis (.pdf).

Cannings, T. I. (2013) Nearest neighbour classification in the tails of a distribution. (.pdf)

Group Members

Jacob Bradley, PhD student, cosupervised with Kevin Myant

Torben Sell, Post-doc

Aristeidis Sionakidis, PhD student, cosupervised with Jonine Figueroa

Some other interesting things

The Centre for Statistics

Maxwell Institute for Mathematical Sciences


Cambridge Cancer Genomics