T. J. Sullivan

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Houman OwhadiProfessor of Applied and Computational Mathematics and Control and Dynamical Systems, CaliforniaVerified email at caltech.edu
Michael OrtizCaltech AND Brown UniversityVerified email at caltech.edu
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
Han Cheng LieProfessor (W1) for Uncertainty Quantification, Universität PotsdamVerified email at uni-potsdam.de
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
Ilja KlebanovPostdoc, Free University of BerlinVerified email at zedat.fu-berlin.de
Alta FangR&D Development Engineer, BIOVIAVerified email at 3ds.com
Philipp HennigUniversity of TübingenVerified email at uni-tuebingen.de
Onur TeymurUniversity of KentVerified email at kent.ac.uk
Andrew M StuartComputing + Mathematical Sciences, CaltechVerified email at caltech.edu
Aretha L TeckentrupReader in Mathematics of Data Science, University of EdinburghVerified email at ed.ac.uk
Hans-Christian HegeZuse Institute Berlin (ZIB)Verified email at zib.de
Christoph von TycowiczHead of Research Group, Zuse Institute BerlinVerified email at zib.de
Florian SchaeferGeorgia TechVerified email at cc.gatech.edu
Björn SprungkTU Bergakademie FreibergVerified email at math.tu-freiberg.de
Hans KerstingResearch Scientist, Yahoo! ResearchVerified email at yahooinc.com
Ufuk TopcuThe University of Texas at AustinVerified email at utexas.edu
Vinod K TewaryNational Institute of Standards & Technology (NIST)Verified email at nist.gov
Lauren RastThe University of Alabama at BirminghamVerified email at uab.edu
Marisol KoslowskiPurdueVerified email at purdue.edu

T. J. Sullivan

University of Warwick

Verified email at warwick.ac.uk - Homepage

Applied Mathematics Applied Probability Uncertainty Quantification Inverse Problems


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Introduction to Uncertainty Quantification TJ Sullivan Texts in Applied Mathematics 63, Springer, 2015	817	2015
Building a Framework for Predictive Science M McKerns, L Strand, TJ Sullivan, A Fang, MAG Aivazis Proceedings of the 10th Python in Science Conference (SciPy 2011), June 2011 …, 2011	234	2011
Bayesian probabilistic numerical methods J Cockayne, CJ Oates, TJ Sullivan, M Girolami SIAM Review 61 (4), 756-789, 2019	190	2019
Optimal Uncertainty Quantification H Owhadi, C Scovel, TJ Sullivan, M McKerns, M Ortiz SIAM Review 55 (2), 271-345, 2013	177	2013
A Modern Retrospective on Probabilistic Numerics CJ Oates, TJ Sullivan Statistics and Computing 29 (6), 1335-1351, 2019	83	2019
On the Brittleness of Bayesian inference H Owhadi, C Scovel, TJ Sullivan SIAM Review 57 (4), 566-582, 2015	79	2015
Brittleness of Bayesian inference under finite information in a continuous world H Owhadi, C Scovel, TJ Sullivan Electronic Journal of Statistics 9 (1), 1-79, 2015	69	2015
Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity F Schäfer, TJ Sullivan, H Owhadi Multiscale Modeling & Simulation 19 (2), 688-730, 2021	62	2021
A rigorous theory of conditional mean embeddings I Klebanov, I Schuster, TJ Sullivan SIAM Journal on Mathematics of Data Science 2 (3), 583–606, 2020	53	2020
Probabilistic numerical methods for PDE-constrained Bayesian inverse problems J Cockayne, C Oates, TJ Sullivan, M Girolami Proceedings of the 36th International Workshop on Bayesian Inference and …, 2017	49	2017
Convergence rates of Gaussian ODE filters H Kersting, TJ Sullivan, P Hennig Statistics and Computing 30 (6), 1791-1816, 2020	44	2020
Well-posed Bayesian inverse problems and heavy-tailed stable quasi-Banach space priors TJ Sullivan Inverse Problems and Imaging 11 (5), 857–874, 2017	44	2017
Strong convergence rates of probabilistic integrators for ordinary differential equations HC Lie, AM Stuart, TJ Sullivan Statistics and Computing 29 (6), 1265-1283, 2019	36	2019
Stratified graphene/noble metal systems for low-loss plasmonics applications L Rast, TJ Sullivan, VK Tewary Physical Review D 87 (4), 045428, 2013	34	2013
Probabilistic meshless methods for partial differential equations and Bayesian inverse problems J Cockayne, C Oates, TJ Sullivan, M Girolami arXiv preprint arXiv:1605.07811, 2016	33	2016
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem M Mollenhauer, N Mücke, TJ Sullivan arXiv preprint arXiv:2211.08875, 2022	32	2022
Implicit probabilistic integrators for ODEs O Teymur, HC Lie, TJ Sullivan, B Calderhead Advances in Neural Information Processing Systems 31 (NIPS 2018), 2018	32	2018
Rigorous Model-Based Uncertainty Quantification with Application to Terminal Ballistics. Part I: Systems with Controllable Inputs and Small Scatter AA Kidane, A Lashgari, B Li, M McKerns, M Ortiz, H Owhadi, ... Journal of the Mechanics and Physics of Solids 60 (5), 983-1001, 2012	31	2012
Geodesic analysis in Kendall’s shape space with epidemiological applications E Nava-Yazdani, HC Hege, TJ Sullivan, C von Tycowicz Journal of Mathematical Imaging and Vision 62 (4), 549-559, 2020	28	2020
Random forward models and log-likelihoods in Bayesian inverse problems HC Lie, TJ Sullivan, AL Teckentrup SIAM/ASA Journal on Uncertainty Quantification 6 (4), 1600–1629, 2018	28	2018

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