Onur Teymur

I am Lecturer in Statistics at the University of Kent, and visiting researcher at the Alan Turing Institute in London.

I am interested in Bayesian statistical theory and, increasingly, in nonparametric Bayesian approaches.

I also work in Bayesian computation, and in particular am involved with the emerging field of probabilistic numerical methods.

How are ya, champ?

Not so bad, thanks for asking.

Three-line CV

I was previously a postdoc at Newcastle University (2020-21), and before that at Imperial College London (2018-20), where I was also a postgraduate (2013-18).

Some publications

1. Teymur, Foley, Breen, Karvonen & Oates (2021) Black Box Probabilistic Numerics; NeurIPS 2021 & arXiv:2106.13718

2. Fisher, Teymur & Oates (2021); GaussED: A Probabilistic Programming Language for Sequential Experimental Design; arXiv:2110.08072[pdf]

3. Cockayne, Graham, Oates, Sullivan & Teymur (2021) Testing whether a learning procedure is calibrated; arXiv:2012.12670[pdf]

4. South, Riabiz, Teymur & Oates (2021) Post-Processing of MCMC; arXiv:2103.16048 (forthcoming in Annual Review of Statistics and Its Application) [pdf]

5. Teymur, Gorham, Riabiz & Oates (2020) Optimal quantisation of probability measures using Maximum Mean Discrepancy; AISTATS 2021 & arXiv:2010.07064[pdf]

6. Teymur & Filippi (2020) A Bayesian nonparametric test for conditional independence; Foundations of Data Science & arXiv:1910.11219[pdf]

7. Teymur, Lie, Sullivan & Calderhead (2018) Implicit probabilistic integrators for ODEs; NeurIPS 2018 & arXiv:1805.07970[pdf]

8. Teymur, Zygalakis & Calderhead (2016) Probabilistic linear multistep methods; NeurIPS 2016 & arXiv:1610.08417[pdf]

October 2021