神马福利影片

• General Information
• Appointments and Positions
• Higher Education, Scientific Degrees, and Professional Certification
• Selected Publications

General Information

Name	Tim Sullivan
Affiliation	Mathematics InstituteLink opens in a new window and School of EngineeringLink opens in a new window, 神马福利影片Link opens in a new window
Address	Mathematics Institute, 神马福利影片, Coventry, CV4 7AL, UK
Email	t.j.sullivan (at) warwick.ac.uk
Website	warwick.ac.uk/tjsullivanLink opens in a new window
Position	Reader in Predictive Modelling

Appointments and Positions

2020–present	and , , Coventry, UK Reader in Predictive Modelling 2025–present Associate Professor 2022–2025 Assistant Professor 2020–2022
2021–2023	, London, UK Turing Fellow
2015–2021	(ZIB), Berlin, Germany Research Group Leader for Uncertainty Quantification
2015–2020	, Berlin, Germany Junior Professor (W1) in Applied Mathematics, with Specialism in Risk and Uncertainty Quantification (Zwischenevaluierung 2018)
2012–2015	, , Coventry, UK Warwick Zeeman Lecturer (Assistant Professor)
2013–2014	, Pasadena, California, USA Visiting Associate in
2012	, Pasadena, California, USA Senior Postdoctoral Scholar in Applied & Computational Mathematics and the
2009–2012	, Pasadena, California, USA Postdoctoral Scholar in Applied & Computational Mathematics and the Postdoctoral Advisors: , PSAAP Center director, and , PSAAP Uncertainty Quantification group leader

Higher Education, Scientific Degrees, and Professional Certification

2022	Fellowship of the Higher Education Academy
2005–2009	Ph.D. in Mathematics by Research, , UK Doctoral Thesis: Analysis of Gradient Descents in Random Energies and Heat Baths Advisor: . Examiners: (University of Bath, UK) and (神马福利影片, UK)
2000–2004	Master of Mathematics (Class I), , UK Advisors: Dr Luca Sbano, academic tutor, and (Technische Universit盲t M眉nchen), dissertation advisor

Selected Publications

See also this list of academic publications.

1. J. Cockayne, C. J. Oates, T. J. Sullivan, and M. Girolami. 鈥淏ayesian probabilistic numerical methods.鈥� SIAM Review 61(4):756–789, 2019.
2. H. C. Lie, T. J. Sullivan, and A. L. Teckentrup. 鈥淩andom forward models and log-likelihoods in Bayesian inverse problems.鈥� SIAM/ASA Journal on Uncertainty Quantification 6(4):1600–1629, 2018.
3. J. Cockayne, C. J. Oates, T. J. Sullivan, and M. Girolami. 鈥淧robabilistic numerical methods for PDE-constrained Bayesian inverse problems鈥� in Proceedings of the 36^th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. G. Verdoolaege. AIP Conference Proceedings 1853:060001-1–060001-8, 2017.
4. T. J. Sullivan. 鈥淲ell-posed Bayesian inverse problems and heavy-tailed stable quasi-Banach space priors.鈥� Inverse Problems and Imaging 11(5):857–874, 2017.
5. T. J. Sullivan. Introduction to Uncertainty Quantification, volume 63 of Texts in Applied Mathematics. Springer, 2015. ISBN 978-3-319-23394-9 (hardcover), 978-3-319-23395-6 (e-book).
6. H. Owhadi, C. Scovel, and T. J. Sullivan. 鈥淥n the brittleness of Bayesian inference.鈥� SIAM Review 57(4):566–582, 2015.
7. H. Owhadi, C. Scovel, and T. J. Sullivan. 鈥淏rittleness of Bayesian inference under finite information in a continuous world.鈥� Electronic Journal of Statistics 9(1):1–79, 2015.
8. T. J. Sullivan, M. McKerns, D. Meyer, F. Theil, H. Owhadi, and M. Ortiz. 鈥淥ptimal uncertainty quantification for legacy data observations of Lipschitz functions.鈥� ESAIM. Mathematical Modelling and Numerical Analysis 47(6):1657–1689, 2013.
9. H. Owhadi, C. Scovel, T. J. Sullivan, M. McKerns, and M. Ortiz. 鈥淥ptimal Uncertainty Quantification.鈥� SIAM Review 55(2):271–345, 2013.
10. M. M. McKerns, L. Strand, T. J. Sullivan, A. Fang, and M. A. G. Aivazis. 鈥淏uilding a Framework for Predictive Science鈥� in Proceedings of the 10th Python in Science Conference (SciPy 2011), June 2011, ed. S. van der Walt and J. Millman. 67–78, 2011.