Dr Tim Sullivan
Associate Professor in Predictive Modelling
Co-Director, Warwick Centre for Predictive Modelling
Turing Fellow (Alan Turing Institute)
Office: Zeeman Building, C2.10
Teaching Responsibilities 2022–2023:
Terms 1–3: Tutorial responsibilities in Mathematics and Engineering
Term 2: ES3J1 Advanced Systems and Software Engineering and MA3H7 Control Theory
For previous years see here.
Keywords: uncertainty quantification, inverse problems, probabilistic numerics, data science
Summary: My mathematical research interests are in uncertainty quantificationLink opens in a new window (UQ), which lies at the intersection of applied mathematics and computational probability. The long-term vision underlying this line of research is to contribute to a paradigm shift in reasoning about complex systems under uncertainty, which is a pressing challenge in many application domains.
Particular topics of interest to me include the theoretical foundations of UQ; non-parametric Bayesian statistics, including inverse problems in function spaces; optimisation-based methods and their relationship to Bayesian methods (e.g. maximum-a-posteriori estimation); and computational methods for applied statistical problems, including dimension reduction and kernel-based machine learning techniques. A point of particular recent focus is probabilistic perspectives on numerical methods themselves, which is an emerging blend of statistical inference and numerical analysis. I have also contributed towards numerical implementation of all of these methods in open-source software packages.
I am a member of SIAMLink opens in a new window and GAMMLink opens in a new window, and have organised sections and minisymposia at multiple international conferences. I am an associate editor of the SIAM/ASA Journal on Uncertainty QuantificationLink opens in a new window.
See also this full list of publications.
- F. Schäfer, T. J. Sullivan, and H. Owhadi. “Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity.” Multiscale Model. Simul. 19(2):688–730, 2021.
doi: 10.1137 /19M129526XLink opens in a new window
- J. Cockayne, C. J. Oates, T. J. Sullivan, and M. Girolami. “Bayesian probabilistic numerical methods.” SIAM Rev. 61(4):756–789, 2019.
doi: 10.1137 /17M1139357Link opens in a new window
- H. C. Lie, T. J. Sullivan, and A. L. Teckentrup. “Random forward models and log-likelihoods in Bayesian inverse problems.” SIAM/ASA J. Uncertain. Quantif. 6(4):1600–1629, 2018.
doi: 10.1137 /18M1166523Link opens in a new window
- J. Cockayne, C. J. Oates, T. J. Sullivan, and M. Girolami. “Probabilistic numerical methods for PDE-constrained Bayesian inverse problems” in Proceedings of the 36th 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.
doi: 10.1063 /1.4985359Link opens in a new window
- 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).
doi: 10.1007 /978 -3 -319 -23395 -6Link opens in a new window
- H. Owhadi, C. Scovel, and T. J. Sullivan. “On the brittleness of Bayesian inference.” SIAM Rev. 57(4):566–582, 2015.
doi: 10.1137 /130938633Link opens in a new window
- H. Owhadi, C. Scovel, and T. J. Sullivan. “Brittleness of Bayesian inference under finite information in a continuous world.” Elec. J. Stat. 9(1):1–79, 2015.
doi: 10.1214 /15 -EJS989Link opens in a new window
- T. J. Sullivan, M. McKerns, D. Meyer, F. Theil, H. Owhadi, and M. Ortiz. “Optimal uncertainty quantification for legacy data observations of Lipschitz functions.” ESAIM. Math. Mod. Num. Anal. 47(6):1657–1689, 2013.
doi: 10.1051 /m2an /2013083Link opens in a new window
- H. Owhadi, C. Scovel, T. J. Sullivan, M. McKerns, and M. Ortiz. “Optimal Uncertainty Quantification.” SIAM Rev. 55(2):271–345, 2013.
doi: 10.1137 /10080782XLink opens in a new window
- M. M. McKerns, L. Strand, T. J. Sullivan, A. Fang, and M. A. G. Aivazis. “Building a Framework for Predictive Science” in Proceedings of the 10th Python in Science Conference (SciPy 2011), June 2011, ed. S. van der Walt and J. Millman. 67–78, 2011.
doi: 10.25080 /Majora -ebaa42b7 -00dLink opens in a new window
Recent Research Grants
- Project 415980428Link opens in a new window “Analysis of maximum a posteriori estimators: Common convergence theories for Bayesian and variational inverse problems”, as PI, funded by the German Research Foundation (DFG)Link opens in a new window, 2020–2022.
- Project TrU-2Link opens in a new window “Demand modelling and control for e-commerce using RKHS transfer operator approaches”, as co-PI, funded by Germany's Excellence StrategyLink opens in a new window, part of the Berlin Mathematics Research Center MATH+Link opens in a new window (EXC-2046/1, project 390685689Link opens in a new window), 2019–2020.
- Project CH-15 “Analysis of Empirical Shape Trajectories”Link opens in a new window, as co-PI, funded by the Einstein Center for Mathematics ECMathLink opens in a new window and the Berlin Mathematics Research Center MATH+Link opens in a new window, 2017–2019.
- Project 337475393Link opens in a new window (SFB1114/A06) “Enabling Bayesian uncertainty quantification for multiscale systems and network models via mutual likelihood-informed dimension reduction”, as project PI, funded by the German Research Foundation (DFG)Link opens in a new window, part of SFB1114 Scaling Cascades in Complex SystemsLink opens in a new window, 2017–2018.