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Research interests:

  • Inverse problems and Bayesian inverse problems
  • Uncertainty quantification
  • Applications in engineering and medicine

Please find more information on Jonas' webpage.

Publications

Analysis of stochastic gradient descent in continuous time
J Latz
– Statistics and Computing
(2021)
31,
39
Classification and image processing with a semi-discrete scheme for fidelity forced Allen–Cahn on graphs
J Budd, Y van Gennip, J Latz
– GAMM Mitteilungen
(2021)
44,
Multilevel Sequential Importance Sampling for Rare Event Estimation
F Wagner, J Latz, I Papaioannou, E Ullmann
– SIAM Journal on Scientific Computing
(2020)
42,
a2062
On the Well-posedness of Bayesian Inverse Problems
J Latz
– SIAM/ASA Journal on Uncertainty Quantification
(2020)
8,
451
Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems
I-G Farcas, J Latz, E Ullmann, T Neckel, H-J Bungartz
– SIAM Journal on Scientific Computing
(2020)
42,
a424
Certified and fast computations with shallow covariance kernels
D Kressner, J Latz, S Massei, E Ullmann
– Foundations of Data Science
(2020)
2,
487
Bayesian Parameter Identification in Cahn--Hilliard Models for Biological Growth
C Kahle, KF Lam, J Latz, E Ullmann
– SIAM/ASA Journal on Uncertainty Quantification
(2019)
7,
526
Fast sampling of parameterised Gaussian random fields
J Latz, M Eisenberger, E Ullmann
– Computer Methods in Applied Mechanics and Engineering
(2019)
348,
978
Multilevel Sequential${}^2$ Monte Carlo for Bayesian Inverse Problems
J Latz, I Papaioannou, E Ullmann
– Journal of Computational Physics
(2018)
368,
154
Generalized Parallel Tempering on Bayesian Inverse Problems
J Latz, JP Madrigal-Cianci, F Nobile, R Tempone
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Research Group

Cambridge Image Analysis

Room

F2.05

Telephone

01223 337917

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