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Research

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Louis Aslett, BA (Mod), PhD

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Assistant Professor, Statistics in the Department of Mathematical Sciences
Telephone: +44 (0) 191 33 43067
Room number: CM212

(email at louis.aslett@durham.ac.uk)

Current Research

My current primary research interest is at the interface between cryptography and statistics, with the focus on privacy preserving statistical analyses. My personal 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.

My other main strand of research is in reliability theory, where interest is in the structural reliability of engineered systems, usually taken from a Bayesian perspective. I also have research interests in computational acceleration of Hidden Markov Models (HMMs) as used in genetics which result in intractable inference as population sizes grow. Threaded through all these research interests is a particular interest in modern massively parallel computing architectures such as GPUs and the development of statistical methodology which is amenable to implementation in such environments.

Research Groups

Department of Mathematical Sciences

Research Interests

  • Cryptography and Privacy in Statistics
  • Reliability Theory
  • Bayesian Statistics
  • MCMC
  • Computational Statistics and High Performance Computing

Indicators of Esteem

  • Invited talks:
    • Isaac Newton Institute, University of Cambridge. Scalable Statistical Inference Workshop, 2017.
    • 3rd UCL Workshop on the Theory of Big Data, 2017.
    • Google Inc., European Headquarters, 2013.

Publications

Conference Paper

  • Esperança, P. M., Aslett, L. J. M. & Holmes, C. C. (2017), Encrypted accelerated least squares regression, in Singh, Aarti & Zhu, Jerry eds, Proceedings of Machine Learning Research 54: The 20th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, Florida, PMLR, Fort Lauderdale, FL, USA, 334-343.
  • Wilson, S. P., Mai, T., Cogan, P., Bhattacharya, A., Robles-Sánchez, O., Aslett, L. J. M., Ó Ríordáin, S. & Roetzer, G. (2014), Using Storm for scaleable sequential statistical inference, in Gilli, Manfred, González-Rodríguez, Gil & Nieto-Reyes, Alicia eds, 21st International Conference on Computational Statistics (COMPSTAT 2014). Geneva, Switzerland, International Association for Statistical Computing, Geneva, 103-109.

Journal Article

Software

Supervises