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, and a Turing Fellow at the Alan Turing Institute. I completed my PhD with Prof Richard Samworth in the Statistical Laboratory at the University of Cambridge in 2015. I also worked with Prof Yingying Fan as a Postdoc at the University of Southern California.

Research interests

• Statistical learning: classification and clustering

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

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

• Applications in genomics

Publications and Preprints

Cannings, T. I. and Fan, Y. (2020) The correlation-assisted missing data estimator. * Preprint. * (.pdf).

Cannings, T. I. (2020) Random projections: Data perturbation for classification problems. * Wiley Interdisciplinary Reviews: Computational Statistics,* 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. (2019) Local nearest neighbour classification with applications to semi-supervised learning. * Ann. Statist.*, to appear. (.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)

Some other interesting things

• Maxwell Institute for Mathematical Sciences

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