Entrance hall of the ICMS Workshop

Theoretical and computational approaches to large scale inverse problems

Dec 02, 2015 - Dec 04, 2015

ICMS, 15 South College Street


Name Institution
Arridge, Simon University College London
Aston, John University of Cambridge
Richtarik, Peter University of Edinburgh
Schönlieb, Carola-Bibiane University of Cambridge
Stuart, Andrew CalTech
Tanner, Jared University of Oxford

Inverse problems are at the heart of data science. The field is cross-­disciplinary both within mathematics, encompassing aspects of pure, applied as well as statistics, and across subjects, including physical sciences, engineering, medicine and biology to name only a few. Inverse problems arise in almost all fields of science when details of a postulated model have to be determined from a set of observed data. With inverse problems, scientists observe an effect and work to determine the cause; the ultimate goal is to find essential information (an object or material properties) that is hidden within the measurements. Biomedical imaging, for instance, gives rise to a variety of inverse problems in which the common goal is to produce an image visualising the interior of a living organism.

Some inversion approaches are based on effective use of a mathematical model in order to make optimal use of the data; other approaches involve model­blind data mining methods. Since inverse problems are concerned with the processing of data and extraction of relevant information, the field is considered part of Information Technology. Inverse problems are mathematically hard, since they are highly nonlinear, ill­posed and present important challenges of deep mathematical interest which are key to the development of reliable and accurate practical solution methods. Often the observed data is noisy, of large scale and high dimensional, and there is a significant challenge in determining the statistical properties of any proposed inversion.

Key scientific question to be answered:​

●  What methods are practical for large scale inverse problems?
●  What sparse representations are useful in practice, and is convex relaxation sufficient for their application in regularisation?
●  What posterior estimates are important for uncertainty quantification ?
●  How can data science in health care profit from approaches based on computational analysis and statistical inference?
●  What probability models can be used in very large dimensional problems where the amount of data is insufficient for robust statistical estimates?
●  What is the correct approach to tackling infinite­dimensional problems : optimise then discretise, or vice­versa?
●  How can deterministic and statistical approaches to optimisation be combined ?
●  Can Physics inform Data Science ? (ie, what PDEs are useful)
●  Can machine learning inform model selection ?
●  How can multiple incommensurate data (e.g. in multimodal imaging) be scaled into a single likelihood measure ?

Key topics to be addressed:​

●  Modeling of the physical phenomena, for the forward problem
●  Analysis of the corresponding inverse problem
●  Identifying appropriate priors from (often large and/or rich) data sets
●  Accurate and efficient numerical and statistical treatment of the forward and inverse problems
●  Working with scientific computation and visualization aids
●  Undertaking experimental verification/model selection
●  Providing summary statistics for approximate inference
●  Development of appropriate priors for regularisation and Bayesian inference

Key sectors involved and impacted:​ Medical & biomedical, geophysical, computer vision, astrophysics, atmospheric physics, high energy physics, solid­state physics, process engineering, weather forecasting, security screening, non­destructive testing.

 This workshop is one of a number of scientific scoping workshops will be held, at the British Library and at other locations across the UK by the Alan Turing Institute. These workshops, which were approved via a competitive process, will map out the national and international data science landscape, focussing on areas core to the Institute’s mission, including computer science and Iinformatics, the mathematical sciences, social science and ethics. They will also serve as an instrument for the development of a number of coherent research programmes. 


This scoping workshop will start at 11.00 on Wednesday 2 December and end at 16.00 on Friday 4 December.


Invited participants will have received an emailed invitation from the Scientific Organisers and an "invitation to register" from ICMS.  The ICMS invitation contains a link to your individual registration form.    


Venue and Talks

The workshop will be held at 15 South College Street, Edinburgh.  Talks will be held in the Newhaven Lecture Theatre.    The Lecture Theatre is equipped with a data projector, computer, visualiser (the new generation of overhead projectors) and two blackboards.  The projector and one board may be used simultaneously.  It is best to bring your presentation on a memory stick to use in our ICMS computer.   Alternatively, it is possible for you to use your own laptop with our data projector, but please be aware that you may have to alter your laptop resolutions/settings. 


UK Visas
If you are travelling from overseas you may require an entry visa. A European visa does not guarantee entry to the UK. Please use this link to the UK Visas site to find out if you need a visa and if so how to apply for one.


Information about travel to the UK and Edinburgh is available here.  Please note that it is your responsibility to have adequate travel insurance to cover medical and other emergencies that may occur on your trip.

A taxi directly from the airport will cost approximately 20.00 GBP to the city centre for a one-way journey.     There is also a bus service direct from the airport to the city centre which will cost 4.50 single or 7.50 GBP return - the Airlink 100.  This is a frequent service (every 10 minutes during peak times) and will bring you close to Waverley Railway Station and the workshop venue. 

Lothian buses charge £1.50 for a single, £4.00 for a day ticket. Please note that the exact fare is required and no change is given.

If travelling by train, please note that Edinburgh has several railway stations; Waverley Railway Station being the main station and closest to the workshop venue at 15 South College Street. If you alight at Edinburgh Waverley, the meeting venue is an easy 10 minute walk over North and South Bridge.  The other railway stations are Haymarket and Edinburgh Park but please note that these stations are at the West End of the city centre.



ICMS will organise accommodation for Invited Participants (if requested on your registration form).



Refreshments throughout the event, lunch each day and a Workshop Dinner one evening will be provided.   


Wireless Access
Access to wifi via Eduroam is available throughout the building. If you are not registered with Eduroam you will be given instructions and a code for accessing the wireless network.  For those without laptops, there will also be a couple of computers available for you to check your emails.  


Registration Fee
There is no registration fee payable for this event. 



This is a provisional programme and may be subject to alteration.

Wednesday 2 December 2015


Registration & coffee/tea in the Chapterhouse, Level 1


Introduction & talks (3)
John Aston (University of Cambridge)
Introductory words as one of the five JV-directors
Ville Kolehmainen (University of Eastern Finland)
Martin Benning (University of Cambridge)
Imaging from incomplete data - applications in MRI, PET and TEM


Lunch in the Chapterhouse, Level 1  


Talks (2)
Marta Betcke (University College London)
Carpe diem: on some dynamic and real time inverse problems
Oliver Dorn (University of Manchester)
Some large scale nonlinear inverse problems in medical, geophysical and industrial imaging


Coffee/tea in the Chapterhouse , Level 1


Talks (2)
Peter Richtarik (University of Edinburgh)
Randomized iterative methods for linear systems
Nick Polydorides
(University of Edinburgh)
Electromagnetic imaging in high-dimensional models


Parallel group discussions on:
The role of inverse problems in data science
Forward modelling of large-scale and high-dimensional inverse problems (physical models and sampling)
Reconstruction guarantees for large-scale, high dimensional and infinite dimensional inverse problems
Uncertainty quantification




Wine reception in the Chapterhouse, Level 1 


Thursday 3 December 2015


Talks (3)
Mike Davies (University of Edinburgh)
Compressed sensing, high dimensional inference and data science
Evren Yarman (Schlumberger)
Large scale inverse problems and challenges in reservoir characterization
Chris Budd (University of Bath)
Adaptivity and large scale meteorological data assimilation 


Coffee/tea in the Chapterhouse, Level 1


Parallel group discussions on:
Sparse approximation; prior selection/influence
Model learning for inverse problems
Multimodal data correlation
Industrial (real-world) inverse problems


Lunch in the Chapterhouse, Level 1




Talks (2)
Richard Hammersley (Emerson Roxar)
Marcelo Pereyra (University of Bristol)  
Bayesian analysis and computation in large-scale imaging inverse problems


Coffee/tea in Chapterhouse, Level 1


Talks (3)
Natalia Bochkina (University of Edinburgh)
Nonregular Bayesian inverse problems
Thomas Blumensath (University of Southampton)
Inverse problems and compressed sending in imaging
Bangti Jin (University College London)


Free time to prepare contribution to written report


Workshop dinner at Blonde Restaurant, 75 St. Leonard's Street, Edinburgh

Friday 4 December 2015


Talks (3)
Coralia Cartis (University of Oxford)
Tractability and scalability of optimization algorithms for nonconvex problems
Yves van Gennip (University of Nottingham)
PDE flavoured inverse problems on networks
Bill Lionheart (University of Manchester) 


Coffee/tea in the Chapterhouse , Level 1


Group discussion on:
Computational challenges for large-scale and high dimensional inverse problems 




Lunch in the Chapterhouse, Level 1


Report Writing



Presentation Details
Benning, Martin
Imaging from incomplete data - applications in MRI, PET and TEM
View Abstract Down
Davies, Mike
Compressed sensing, high dimensional inference and data science
View Abstract Down
Dorn, Oliver
Some large scale nonlinear inverse problems in medical, geophysical and industrial imaging
View Abstract Down
Polydorides, Nick
Electromagnetic imaging in high-dimensional models
View Abstract Down
van Gennip, Yves
PDE flavoured inverse problems on networks
View Abstract Down


Name Institution
Arridge, Simon University College London
Aston, John University of Cambridge
Belyaev, Alexander Heriot-Watt University
Benning, Martin University of Cambridge
Betcke, Marta University College London
Blumensath, Thomas University of Southampton
Bochkina, Natalia University of Edinburgh
Branicki, Michal University of Edinburgh
Brown, Malcolm Cardiff University
Budd, Chris University of Bath
Cartis, Coralia University of Oxford
Chen, Wei University of Cambridge
Davies, Mike University of Edinburgh
Dorn, Oliver University of Manchester
Ehrhardt, Matthias University College London
Golbabaee, Mohammad EPFL
Gower, Robert University of Edinburgh
Hammersley, Richard Roxar, Emerson Process Management
Houssineau, Jeremie Heriot-Watt University
Idowu, Michael Abertay University
Jin, Bangti University College London
Kamilis, Dimitris University of Edinburgh
Kolehmainen, Ville University of Eastern Finland
Kueh, Audrey Warwick University
Lazic, Jasmina Mathworks
Lesnic, Daniel University of Leeds
Lionheart, Bill Manchester University
Lucka, Felix University College London
Mason, Jonathan University of Edinburgh
Neumann-Brosig, Matthias IAV GmbH
Perelli, Alessandro University of Edinburgh
Pereyra, Marcelo Heriot-Watt University
Polydorides, Nick University of Edinburgh
Powell, Keith Kromek
Richtarik, Peter University of Edinburgh
Rodrigues, Jenovah University of Edinburgh
Schönlieb, Carola-Bibiane University of Cambridge
Tanner, Jared University of Oxford
Tsaftaris, Sotirios University of Edinburgh
Uney, Murat University of Edinburgh
van Gennip, Yves University of Nottingham
Wiaux, Yves Heriot-Watt University/University of Edinburgh
Yaghoobi, Mehrdad University of Edinburgh
Yarman, Can Evren Schlumberger Gould Research