Timothy I. Cannings

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Timothy I. Cannings

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Contact:

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. (2021) Data-driven design of targeted gene panels for estimating immunotherapy biomarkers. * Preprint. * (.pdf) The accompanying ** R ** package ** ICBioMark ** is available from CRAN.

Cannings, T. I. and Fan, Y. (2022+) The correlation-assisted missing data estimator. *J. Mach. Learn. Res.*, to appear. (.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

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

• Maxwell Institute for Mathematical Sciences

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