Compressive Learning

A classical problem in data analysis is how to compress the size of data without destroying its salient information. This is becoming increasingly important in the era of big data where we need to process ever increasing quantities of data of various modalities. Some exciting new developments in this regard have recently emerged exploiting ideas such as kernel representations [4] and ‘sketching’ [5] in new "compressive learning" algorithms [1, 2, 3].

This project will develop and analyse new algorithms for compressive learning and investigate their computational and statistical efficiency in processing big data. The studentship forms a key component of the advanced ERC project C-SENSE, "Exploiting Low Dimensional Models in Sensing, Computation and Processing".

The successful candidate will be located in the Prof Davies’ research group in the Institute for Digital Communications (IDCOM). There will also be opportunities to collaborate with the University of Edinburgh’s School of Mathematics and the Alan Turing Institute for Data Science.

  1. N. Keriven, A. Bourrier, R. Gribonval, P. Perez. Sketching for Large Scale Learning of Mixture Models. ICASSP, 2016
  2. A. Rahimi and B. Recht, Random Features for Large Scale Kernel Machines. Advances in Neural Information Processing Systems, no. 1, 2007
  3. H. Reboredo, F. Renna, R. Calderbank, and M. D. Rodrigues, Compressive Classification. Proc. GlobalSIP, no. 1, 2013
  4. B. Scholkopf and A. Smola, Learning with Kernels. MIT Press, Cambridge, MA, 2002
  5. D. Woodruff, Sketching as a Tool for Numerical Linear Algebra. Foundations and Trends in Theoretical Computer Science Vol. 10, No. 1-2 (2014)

Further Information: 

For more information on Prof Davies’ research see:

To apply please go the University's Jobs page and search for vacancy number 040772 -

Closing Date: 

Friday, September 8, 2017
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European Research Council logo

Principal Supervisor: 


The ideal candidate should have a degree in electrical engineering/computer science or applied mathematics/statistics with a strong interest in data science and machine learning.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.


The student will be employed as a Research Associate on the EC research grant (ERC: C-SENSE) and are responsible for payment of their fees. Fees are an ineligible cost on EC grants.

Further information and other funding options.

Informal Enquiries: