Big Data Inverse Problems

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Big Data Inverse Problems

 20 - 24 May 2024

ICMS, Bayes Centre, Edinburgh


Scientific organisers

  • Matthias Chung, Emory University
  • Matthias Ehrhardt, University of Bath
  • Carola Bibiane Schönlieb, University of Cambridge


The rapidly evolving field of data science recognizes the urgent need for novel computational methods to overcome challenges of inference and uncertainty quantification to make informed decisions in big data settings. Emerging fields such as data analytics, machine learning, and uncertainty quantification rely heavily on efficient computational methods for inverse problems.  

This workshop will gather researchers from the intersecting fields of inverse problems, uncertainty quantification, data analytics, machine learning, and related areas to discuss novel theory and new methods for big data challenges. A focused multifaceted research group gathered at one location is an excellent opportunity for established and early career researchers. The aim of this workshop is to foster a diverse, open, and friendly environment to advance this field, open new directions, and to stimulate new collaboration. 



MONDAY 20 MAY 2024
10:00-10:20 Registration and Refreshments
10:20-10:30 Welcome and Housekeeping
10:30-11:00 Per Christian Hansen, DTU AB- and BA-GMRES Methods for X-Ray CT with an Unmatched Back Projector
11:00-11:30 Marcelo Peyrera, Heriot-Watt University Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems
11:30-12:00 Jonas Latz, University of Manchester Losing momentum in continuous-time stochastic optimisation
12:00-14:00 Lunch
14:00-14:30 Anupam Gumber, Machine Learning Genoa Centre, University of Genoa Generative Fourier neural operators for non linear inverse problems
14:30-15:00 Tia Chung, Emory University Big Data Inverse Problems — Promoting Sparsity and Learning to Regularize
15:00-15:30 Refreshments
15:30-16:00 Tobias Wolf, University of Klagenfurt Nested Bregman Iterations for image decomposition
16:00-16:30 Luca Calatroni, CNRS Bregman relaxation of $\ell_0$-regularized criteria with general data terms
17:00-18:00 Welcome Reception, hosted at ICMS
9:15-9:45 Simon Arridge, UCL Learned image flows with equivariance constraints
9:45-10:15 Ferdia Sherry, University of Cambridge Designing Stable Neural Networks using Convex Analysis and ODEs
10:15-10:45 Refreshments
10:45-11:15 Francesco Tudisco, University of Edinburgh Exploiting low-rank geometry in deep learning
11:15-11:45 Mohammad Sadegh Salehi, University of Bath An adaptively inexact first-order method for bilevel learning
11:45-12:15 Carola-Bibiane Schönlieb, University of Cambridge Machine learned regularisation for inverse problems - the dos and don’ts
12:15-14:15 Lunch
14:15-14:45 Yves Wiaux, Heriot-Watt University R2D2: a deep neural network series for ultra-fast large-scale imaging in radio astronomy (but not only)
14:55-15:15 Tan Bui-Thanh, University of Texas, Austin An autoencoder compression approach for accelerating large-scale inverse problems
15:15-15:45 Refreshments
15:45-16:15 Elizabeth Newman, Emory University Optimal Tensor Algebras for Efficient Data Representation
16:15-17:00 Young Researcher Gathering; Carola + Panel discussion
9:15-9:45 Samuli Siltanen, University of Helsinki Virtual X-rays: parallel-beam tomography hidden within electric probing
9:45-10:15 Patrick Fahy, University of Bath Learning preconditioners for inverse problems
10:15-10:45 Refreshments
10:45-11:15 Ronny Ramlau, Johannes Kepler University Linz Reconstructability issues for the atmospheric tomography problem in adaptive optics for earth bound telescopes
11:15-11:45 Markus Rau, Newcastle University Mapping the Universe: a Big-Data Inverse Problem
11:45-12:15 Silvia Gazzola, University of Bath Automatic space-variant anisotropic Tikhonov regularization through bilevel optimization
12:15-14:15 Lunch
14:15-14:45 Matthias Ehrhardt, University of Bath Stochastic Optimisation for Large-Scale Inverse Problems
14:55-15:15 Kostas Papafitsoros, Queen Mary University of London Learned unrolled schemes for estimating spatio-temporally varying regularisation parameters in image reconstruction
15:15-15:45 Refreshments
15:45-16:15 Audrey Repetti, Heriot-Watt University TBA primal-dual plug-and-play algorithm for computational optical imaging (Joint work with C. Garcia, R. R. Thomson, and J.-C. Pesquet)C
16:15-16:45 Martin Benning, UCL Efficient estimation of optimal sampling patterns for MRI
16:45-18:00 Discussions and collaboration
18:00-19:30 Samuli Siltanen, Public Lecture, hosted in G.03 (ground floor), University of Helsinki The Magic of Maths: 3D X-ray vision
9:15-9:45 Speaker, Institution
9:45-10:15 Speaker, Institution
10:15-10:45 Refreshments
10:45-11:15 Tatiana Bubba, University of Bath Sparse dynamic tomography regularization using optimal space-time priors
11:15-11:45 Matthew Li, MIT Dimension Reduction for Statistical Inference via (Dimensional)
11:45-12:15 Zeljko Kereta, UCL Image Reconstruction in Deep Image Prior Subspaces
12:15-14:15 Lunch
14:15-14:45 Hong Ye Tan , University of Cambridge Data-driven geometry for convex optimization
14:45-15:15 Yiqiu Dong, DTU Sampling strategies in sparse bayesian inference
15:15-15:45 Refreshments
15:45-16:15 Markus Holzleitner, University of Genoa Nonlinear functional regression
16:15-16:45 Felix Lucka, CWI Amsterdam Learning for X-ray Computed Tomography
16:45-17:15 Rebecca Willett, University of Chicago Learned Inverse Scattering Inspired by Recursive Linearization
Workshop Dinner
FRIDAY 24 MAY 2024
9:15-9:45 James Nagy, Emory University Mixed Precision Arithmetic for Large Scale Inverse Problems
9:45-10:15 Mirjeta Pasha, Virginia Tech Modern Challenges in Large-Scale and High Dimensional Data Analysis
10:15-10:45 Refreshments
10:45-11:15 Jakob Sauer Jorgensen, DTU CUQIpy: Computational Uncertainty Quantification for Inverse Problems in Python
11:15-11:45 Marta Betcke, UCL Learned Stochastic Primal Dual method with applications in subsampled and low dose CT
11:45-12:15 Eric de Sturler, Virginia Tech Streaming Methods for Inverse Problems
12:15 End of workshop