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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. The event will welcome industrial representatives, doctoral students, early career and established academics working in this field to attend.

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 problems;
  • Optimisation, statistical, and machine learning methods for solving inverse problems;
  • Mathematical theory for inverse problems;
  • Deterministic and stochastic computational methods and algorithms.

Further information is available on the event website.


Tuesday, 3 May, 2022 - 09:00 to Thursday, 5 May, 2022 - 17:00
Event location: 
ICMS, Edinburgh

About Us

The Cambridge Mathematics of Information in Healthcare Hub (CMIH) is a collaboration between mathematics, statistics, computer science and medicine, aiming to develop robust and clinically practical data analytics algorithms for healthcare decision making. Our work focusses on some of the most challenging public health problems; Cancer, Cardiovascular Disease, and Dementia.




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