PROGRAMME
ABSTRACT BOOK
Inverse problems are widespread in many varied fields such as medical and satellite imaging, biology, astronomy, geophysics, environmental sciences, computer vision, energy, finance, and defence. These problems are inverse in the sense that they arise from seeking to use a mathematical or physical model “backwards” to indirectly determine a quantity of interest from the effect that this quantity causes on some observed data.
A main challenge resulting from using models “backwards” to measure causes from their effects is that solutions are often not well posed, i.e., not unique and/or unstable with respect to small perturbations in the data. This difficulty has stimulated an important amount of research and innovation at the interface of applied mathematics, statistics, engineering, physics, and other fields, leading to great social and economic benefit through impact on science, medicine, and engineering.
The aim of this conference is to bring together the applied mathematics, statistics, machine learning, engineering, physics and industrial communities around the topic of inverse problems to discuss recent developments and open challenges in theory, methodology, computational algorithms, and applications. We welcome industrial representatives, doctoral students, early career and established academics working in this field to attend.
Topic list
Topics of interest include, for example,
- Inverse problems in mathematical and computational imaging.
- Inverse problems in science, medicine, engineering, and other fields.
- Model‐based and data‐driven methods for solving inverse
- Optimisation, statistical, and machine learning methods for solving inverse problems.
- Mathematical theory for inverse problems.
- Deterministic and stochastic computational methods and algorithms.
Invited Speakers
Thomas Pock (Graz University of Technology, Austria)
Gabriele Steidl (Berlin Institute of Technology, Germany)
Jason McEwen (University College London & Kagenova)
Yi Yu (University of Warwick, UK)
Andrew Duncan (Imperial College / Improbable London, UK)
Luca Calatroni (CNRS & Nice University, France)
Detecting and localising changes in complex environments, Yi Yu.
In applications ranging from climate monitoring to surveillance, we often wish to estimate when the distribution generating data in a time series has changed. This task, referred to as change point detection and localisation, is particularly challenging when data is high-dimensional and reflects complex behaviour. In this talk, I will first describe recent advances in this field. Time series data are in the form of sequential data collecting in a one-dimensional chain graph. It is natural to go beyond one-dimensional chain graph to arbitrary-dimensional grid graphs, or even general graphs. I will then present recent results in partition recovery in such more challenging settings, which can be seen as extensions of change point detection and localisation in these general graphs.
Seeing through light: sparse and learning methods for super-resolution fluorescence microscopy, Luca Calatroni
Super-resolution fluorescence microscopy techniques overcome the physical barrier of light diffraction allowing for the observation of indistinguishable sub-pixel entities of high relevance in several biological imaging applications. State-of-the-art methods achieve adequate spatio-temporal resolution under challenging experimental conditions by means either of costly devices and/or specific fluorescent molecules. Over the last decade, a major effort in the field has thus been made in order to develop `democratic’ super-resolution techniques able to adapt to common microscopes and conventional fluorescent dyes. From an inverse problem perspective, encoding prior structural assumptions on the samples observed and/or data-driven information can be beneficial in this respect. In this talk, we focus on a model for 2D and 3D super-resolution microscopy based on sparsity and non-convex optimisation. Two approaches are considered: in the former, the sparsity assumption on the fluorescent molecules as well as their temporal and spatial independence are integrated in a covariance domain, where a sparse and non-convex optimisation problem is formulated for retrieving molecule locations precisely. In the latter approach, a Generative Adversarial Network (GAN) is used to estimate directly intensity information by comparisons with training data and by means of a physically-inspired generative network.
The proposed approaches are able to retrieve noise and background information as well as sample intensities, a valuable piece of information for 3D super-resolution imaging. Automatic parameter selection strategies based on algorithmic restarting and discrepancy-type techniques are discussed and several results both for simulated and real data are reported as well as comparisons with state-of-the-art approaches.
Registration
Due to the current social distancing restrictions. Registration is only available for those presenting at the conference.
Please contact the conferences department in order to receive the accommodation discount code for Edinburgh First Hotel.
If you are attending the conference please use the hashtag #IMAInverseProblems2022 and tag the IMA on socials!
Conference Fees
Conference fees include access to the Conference, refreshments and lunches and a Delegate pack.
Early Bird Fee – IMA Member £200
Early Bird Fee – Non Member £265
Early Bird Fee – IMA Student £100
Early Bird Fee – Non Member Student £120
Early Bird Fees will be available until 9th of April after which fees will increase by £20.
Conference Dinner 4 May – £45
*Conference fees include refreshments and lunches throughout the conference.
Organising Committee
Marcelo Pereyra (Heriot‐Watt University & Maxwell Institute) ‐ Conference Chair
Yoann Altmann (Heriot‐Watt University)
Konstantinos Zygalakis (University of Edinburgh & Maxwell Institute)
Mike Davies (University of Edinburgh)
Scientific Committee
Simon Arridge (University College London)
Marta Betcke (University College London)
Martin Benning (Queen Mary University of London)
Thomas Blumensath (University of Southampton)
Tatiana Bubba (University of Bath)
Julie Delon (Paris Descartes University)
Matthias Ehrhardt (University of Bath)
Jean‐François Giovannelli (University of Bordeaux)
Sean Holman (University of Manchester)
Clifford Nolan (University of Limerick)
Clarice Poon (University of Bath)
Audrey Repetti (Heriot‐Watt University & Maxwell Institute)
Important information
We are aware of a very convincing scam targeting participants in mathematical research events. These scammers may telephone or email you and tell you that they are organising accommodation in Edinburgh for you on behalf of IMA/ICMS. If you are approached by a third party (eg Business Travel Management) asking for booking or payment details, please ignore. IMA/ICMS will never ask you for credit/debit card information. The IMA are organising this conference and will be the only organisation to take registration details and organise any additional requirements. If you are unsure on anything please email our conferences department directly conferences@ima.org.uk.
COVID
We plan to run conferences in person as advertised; however, if government guidance changes then we will consider holding affected events online using Zoom.
Contact information
For scientific queries please contact: Marcelo Pereyra (Heriot Watt University) m.pereyra@hw.ac.uk
For general conference queries please contact the Conferences Department, Institute of Mathematics and its Applications, Catherine Richards
House, 16 Nelson Street, Southend‐on‐Sea, Essex, SS1 1EF, UK.
Email: conferences@ima.org.uk Tel: +44 (0) 1702 354 020
