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SUMMARY:CIL: Open-Source Optimisation for Imaging Inverse Problems - Evang
 elos Papoutsellis
DTSTART;TZID=Europe/Paris:20261126T105000
DTEND;TZID=Europe/Paris:20261126T112000
DTSTAMP:20260930T112507Z
UID:pretalx-compute-paris-2026-U37AQN@pretalx.com
DESCRIPTION:Inverse problems arise when the quantity of interest cannot be
  measured directly\, but must be estimated from indirect\, noisy\, incompl
 ete\, or degraded data. In scientific imaging\, this includes tomographic 
 reconstruction for medical imaging and materials science\, denoising\, deb
 lurring\, and inpainting. Although these applications look different\, the
 y share a common mathematical structure: recover an unknown image or volum
 e by balancing agreement with the measured data against prior knowledge. T
 his makes optimisation a central tool for solving inverse problems\, but a
 lso creates a software challenge. How do we translate mathematical formula
 tions into working code? How do we test different regularisers\, constrain
 ts\, and solvers? How do we compare optimisation strategies fairly and mak
 e experiments reproducible across datasets and applications? This talk pre
 sents the [Core Imaging Library (CIL)](https://github.com/TomographicImagi
 ng/CIL)\, an open-source Python framework for optimisation-based workflows
  in inverse problems. CIL provides composable tools for combining data rea
 ders\, operators\, objective functions and algorithms into transparent com
 putational pipelines. The talk will show how CIL helps users prototype new
  methods\, compare algorithms\, and build reproducible workflows for appli
 cations in medical imaging\, materials science\, and other data intensive 
 imaging modalities.
LOCATION:Room 106
URL:https://pretalx.com/compute-paris-2026/talk/U37AQN/
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