Onur Teymur

I was latterly Lecturer in Statistics at the University of Kent (2021-23), as well as visiting researcher at the Alan Turing Institute in London.

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

I am no longer an active academic researcher but I remain happy to help if anybody interested in my work gets in touch with questions, and I would be thrilled to learn of any novel developments or applications relevant to my academic work.

Some publications

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

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

3. Cockayne, Graham, Oates, Sullivan & Teymur (2022) Testing whether a learning procedure is calibrated; Journal of Machine Learning Research & arXiv:2012.12670 [pdf]

4. South, Riabiz, Teymur & Oates (2022) Post-Processing of MCMC; Annual Review of Statistics and its Application & arXiv:2103.16048 [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]

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Thesis: Teymur (2018) Topics in the probabilistic solution of Ordinary Differential Equations; Imperial College London [pdf]