The mathematics of imaging: the CIRM pre-school


7-11 January 2019

This Winter school is intended to be a pre-school for the thematic trimester “The mathematics of Imaging”, that will be held in Paris at the IHP (Institut Henri Poincaré), from January 7 to April 5, 2019.

This pre-school will take place at the CIRM (Centre International de Rencontres Mathématiques), Marseille, the week January 7-11, 2019.

Pre-registration

You can pre-register on the CIRM website of the school.

Program of the school

The pre-school will contain courses, practical sessions, flash presentations and poster sessions.

The 4 main courses will be:

Agenda

MONDAY

  • 9h00-10h30 - Convex methods in imaging
  • 10h30-11h - Coffee break
  • 11h-12h30 - Convex methods in imaging
  • 12h30-14h - Lunch
  • 14h-15h30 - Sparsity in imaging
  • 15h30-16h - Coffee break
  • 16h00 -17h30 - Sparsity in imaging
  • Evening - Poster session (TBC)

TUESDAY

  • 9h00-10h30 - Convex methods in imaging
  • 10h30-11h - Coffee break
  • 11h-12h30 - Convex methods in imaging
  • 12h30-14h - Lunch
  • 14h-15h30 - Sparsity in imaging
  • 15h30-16h - Coffee break
  • 16h00 -17h30 - Sparsity in imaging
  • Evening - Poster session (TBC)

WEDNESDAY

  • 9h00-10h30 - Bayesian methods in imaging
  • 10h30-11h - Coffee break
  • 11h-12h30 - Bayesian methods in imaging
  • 12h30-14h - Lunch
  • Free afternoon
  • Evening - TBA

THURSDAY

  • 9h00-10h30 - Bayesian methods in imaging
  • 10h30-11h - Coffee break
  • 11h-12h30 - Bayesian methods in imaging
  • 12h30-14h - Lunch
  • 14h-15h30 - Practical machine learning
  • 15h30-16h - Coffee break
  • 16h00 -17h30 - Practical machine learning

FRIDAY

  • 9h00-10h30 - Practical machine learning
  • 10h30-11h - Coffee break
  • 11h-12h30 - Practical machine learning
  • 12h30-14h - Lunch

Abstracts

Daniel Cremers (Technische Universität München), convex methods in imaging: TBA

Clarice Poon (University of Cambridge): sparsity in imaging: In the last few decades sparsity has become ubiquitous and is often one of the key assumptions behind imaging methods. In this course, we will discuss how sparsity arises in imaging (in particular, wavelets) and some ways in which sparsity has been exploited (in particular, compressed sensing and super resolution of measures).

Marcelo Pereyra (Heriot-Watt University, Edinburgh): Bayesian methods in imaging: This course presents an overview of modern Bayesian strategies for solving imaging inverse problems. We will start by introducing the Bayesian statistical decision theory framework underpinning Bayesian analysis, and then explore efficient numerical methods for performing Bayesian computation in large-scale settings. We will pay special attention to high-dimensional imaging models that are log-concave w.r.t. the unknown image, related to so-called “convex imaging problems”. This will provide an opportunity to establish connections with the convex optimisation and machine learning approaches to imaging, and to discuss some of their relative strengths and drawbacks. Examples of topics covered in the course include: efficient stochastic simulation and optimisation numerical methods that tightly combine proximal convex optimisation with Markov chain Monte Carlo techniques; strategies for estimating unknown model parameters and performing model selection, methods for calculating Bayesian confidence intervals for images and performing uncertainty quantification analyses; and new theory regarding the role of convexity in maximum-a-posteriori and minimum-mean-square-error estimation. The theory, methods, and algorithms are illustrated with a range of mathematical imaging experiments.

Alexandre Gramfort (INRIA, Parietal Team, Université Paris-Saclay), Practical machine learning: What you will learn in this course:

  • how fundamental hypothesis of statistical learning theory are challenged in practice (violation of independence, covariate shifts etc.) ;
  • what are the most successful models in data challenges and how they work ;
  • how to automate model search and hyperparameter selection ;
  • learn about great packages for machine learning using Python ;
  • how to conduct rigorous machine learning experiments by competing on a data challenge.

Scientific Committee