Scientific organisers
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Matthias Chung, Emory University
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Matthias Ehrhardt, University of Bath
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Carola Bibiane Schönlieb, University of Cambridge
About:
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.
Programme:
| MONDAY 20 MAY 2024 | ||
| Registration and Refreshments | ||
| Welcome and Housekeeping | ||
| Per Christian Hansen, DTU | AB- and BA-GMRES Methods for X-Ray CT with an Unmatched Back Projector | |
| Marcelo Peyrera, Heriot-Watt University | Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems | |
| Jonas Latz, University of Manchester | Losing momentum in continuous-time stochastic optimisation | |
| Lunch | ||
| Anupam Gumber, Machine Learning Genoa Centre, University of Genoa | Generative Fourier neural operators for non linear inverse problems | |
| Tia Chung, Emory University | Big Data Inverse Problems — Promoting Sparsity and Learning to Regularize | |
| Refreshments | ||
| Tobias Wolf, University of Klagenfurt | Nested Bregman Iterations for image decomposition | |
| Luca Calatroni, CNRS | Bregman relaxation of $\ell_0$-regularized criteria with general data terms | |
| Welcome Reception, hosted at ICMS | ||
| TUESDAY 21 MAY 2024 | ||
| Simon Arridge, UCL | Learned image flows with equivariance constraints | |
| Ferdia Sherry, University of Cambridge | Designing Stable Neural Networks using Convex Analysis and ODEs | |
| Refreshments | ||
| Francesco Tudisco, University of Edinburgh | Exploiting low-rank geometry in deep learning | |
| Mohammad Sadegh Salehi, University of Bath | An adaptively inexact first-order method for bilevel learning | |
| Carola-Bibiane Schönlieb, University of Cambridge | Machine learned regularisation for inverse problems - the dos and don’ts |
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| Lunch | ||
| Yves Wiaux, Heriot-Watt University | R2D2: a deep neural network series for ultra-fast large-scale imaging in radio astronomy (but not only) | |
| Tan Bui-Thanh, University of Texas, Austin | An autoencoder compression approach for accelerating large-scale inverse problems | |
| Refreshments | ||
| Elizabeth Newman, Emory University | Optimal Tensor Algebras for Efficient Data Representation | |
| Young Researcher Gathering; Carola + Panel discussion | ||
| WEDNESDAY 22 MAY 2024 | ||
| Samuli Siltanen, University of Helsinki | Virtual X-rays: parallel-beam tomography hidden within electric probing | |
| Patrick Fahy, University of Bath | Learning preconditioners for inverse problems | |
| Refreshments | ||
| Ronny Ramlau, Johannes Kepler University Linz | Reconstructability issues for the atmospheric tomography problem in adaptive optics for earth bound telescopes | |
| Markus Rau, Newcastle University | Mapping the Universe: a Big-Data Inverse Problem | |
| Silvia Gazzola, University of Bath | Automatic space-variant anisotropic Tikhonov regularization through bilevel optimization | |
| Lunch | ||
| Matthias Ehrhardt, University of Bath | Stochastic Optimisation for Large-Scale Inverse Problems |
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| Kostas Papafitsoros, Queen Mary University of London | Learned unrolled schemes for estimating spatio-temporally varying regularisation parameters in image reconstruction | |
| Refreshments | ||
| 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 | |
| Martin Benning, UCL | Efficient estimation of optimal sampling patterns for MRI | |
| Discussions and collaboration | ||
| Samuli Siltanen, Public Lecture, hosted in G.03 (ground floor), University of Helsinki | The Magic of Maths: 3D X-ray vision | |
| THURSDAY 23 MAY 2024 | ||
| Speaker, Institution | ||
| Speaker, Institution |
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| Refreshments | ||
| Tatiana Bubba, University of Bath | Sparse dynamic tomography regularization using optimal space-time priors | |
| Matthew Li, MIT | Dimension Reduction for Statistical Inference via (Dimensional) | |
| Zeljko Kereta, UCL | Image Reconstruction in Deep Image Prior Subspaces | |
| Lunch | ||
| Hong Ye Tan , University of Cambridge | Data-driven geometry for convex optimization | |
| Yiqiu Dong, DTU | Sampling strategies in sparse bayesian inference | |
| Refreshments | ||
| Markus Holzleitner, University of Genoa | Nonlinear functional regression | |
| Felix Lucka, CWI Amsterdam | Learning for X-ray Computed Tomography | |
| Rebecca Willett, University of Chicago | Learned Inverse Scattering Inspired by Recursive Linearization | |
| Workshop Dinner | ||
| FRIDAY 24 MAY 2024 | ||
| James Nagy, Emory University | Mixed Precision Arithmetic for Large Scale Inverse Problems | |
| Mirjeta Pasha, Virginia Tech | Modern Challenges in Large-Scale and High Dimensional Data Analysis | |
| Refreshments | ||
| Jakob Sauer Jorgensen, DTU | CUQIpy: Computational Uncertainty Quantification for Inverse Problems in Python | |
| Marta Betcke, UCL | Learned Stochastic Primal Dual method with applications in subsampled and low dose CT | |
| Eric de Sturler, Virginia Tech | Streaming Methods for Inverse Problems | |
| End of workshop |