Louis Aslett

Louis Aslett

Encrypted statistical methods, parallel MCMC methods, high performance computing, reliability theory

Louis Aslett is a postdoc on the EPSRC funded i-like project in Chris Holmes’ group, and a Junior Research Fellow at Corpus Christi College.

The objective of i-like is to further develop computational methodologies for tackling complex likelihood based inference problems, where calculation of the likelihood may be prohibitively slow, or it is simply impossible to evaluate.

Louis’ current primary research interest within i-like is at the interface between cryptography and statistics, with the focus on privacy preserving statistical analyses. His interest is on the statistics side of this fusion, developing novel statistical methodology which is amenable to use in the constrained environment of encrypted computation made possible by recent developments in homomorphic encryption.



  • P. M. Esperança, L. J. Aslett, C. C. Holmes, Encrypted accelerated least squares regression, arXiv preprint arXiv:1703.00839, 2017.


  • L. J. Aslett, P. M. Esperança, C. C. Holmes, Encrypted statistical machine learning: new privacy preserving methods, arXiv preprint arXiv:1508.06845, 2015.
  • L. J. Aslett, P. M. Esperança, C. C. Holmes, A review of homomorphic encryption and software tools for encrypted statistical machine learning, arXiv preprint arXiv:1508.06574, 2015.