Marcelo Pereyra | Welcome

Maxwell Insitute for Mathematical Sciences
School of Mathematical and Computer Sciences, Heriot-Watt University

News

EPSRC Project BLOOM I am delighted to report that the UKRI EPSRC research grant proposal Bayesian computation for low-photon imaging with Dr Yoann Altmann and Kostas Zygalakis has been successful! This is a 4-year project to develop new computational imaging methodology for low-photon imaging problems, with special attention to quantum-enhanced approaches that seek to exploit the quantum nature of light in order to dramatically advance imaging sciences. In collaboration with the UK Quantum Enhanced Imaging Hub (QUANTIC), the University of Illinois, Ecole Normale Superieure Paris-Saclay, and the aerospace company Leonardo. More details here.

Conference I will have the pleasure of chairing the next IMA conference on Inverse Problems, which will take place in Edinburgh at the ICMS from 3 to 5 May 2022. More details soon!

Three new companion papers Following several years of intense work, Ana F. Vidal, Valentin De Bortoli, Alain Durmus and I have produced these three long companion papers on new computation methodology for empirical Bayesian estimation in high-dimentional inverse problems, with special attention to imaging applications:
1) A. F. Vidal, V. De Bortoli, M. Pereyra, A. Durmus, "Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments", SIAM Journal on Imaging Sciences, vol. 13, no. 4, pp. 1945-1989, 2020.
2) V. De Bortoli, A. Durmus, A. F. Vidal, M. Pereyra, "Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part II: Theoretical Analysis", SIAM Journal on Imaging Sciences, vol. 13, no. 4, pp. 1990-2028, 2020.
3) V. De Bortoli, A. F. Vidal, A. Durmus, M. Pereyra, "Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation", Statistics and Computing, to appear. Pre-print https://arxiv.org/abs/1906.12281.

EPSRC Project BOLT I have started an exciting three-year project, funded by EPSRC, to develop new Bayesian methodology for blind and semi-blind bilinear inverse problems in imaging sciences, in collaboration with colleagues at Ecole Normale Superieure Cachan and at UCL Mullard Space Science Laboratory. The aim is to develop new computational and analysis methods for objectively comparing, selecting, and calibrating computational imaging models directly from the observed data in a fully unsupervised way. More details here.

New paper I am glad to report that the paper "Wasserstein Control of Mirror Langevin Monte Carlo" with Kelvin Zhang, Gabriel Peyré, and Jalal Fadili has been accepted for publication in COLT 2020. See http://proceedings.mlr.press/v125/zhang20a.html.

New paper The paper "Accelerating proximal Markov chain Monte Carlo by using explicit stabilised methods" with Luis Vargas and Kostas Zygalakis has now been published in SIAM J. Imaging Sciences, Vol. 13, No. 2, pp. 905–935.

© 2017 Marcelo Pereyra
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