The CCIMI and CMIH are pleased to be co-sponsoring the Cambridge – Heriot Watt interdisciplinary data science workshop on “Mathematical imaging with partially unknown models” at Jesus College in Cambridge, 20-21 Feb. 2017. More details on the programme will be confirmed at a later date.
The workshop is organised by Marcelo Pereyra (Assistant professor, School of Mathematical and Computer Sciences, Heriot Watt) and Carola-Bibiane Schönlieb (Head of the Cantab Capital Institute for the Mathematics of Information (CCIMI), Cambridge), alongside local organiser Martin Benning (Department of Applied Mathematics and Theoretical Physics, Cambridge)
The workshop aims to gather an interdisciplinary group of leading imaging experts from the applied analysis, statistics, and signal processing communities around the topic of “imaging with partially unknown models”. The goal is to promote synergy and cross-fertilisation between these communities and set the basis for a multidisciplinary approach to the problem.
Mathematical imaging is at the core of modern data science, with important applications in medicine, biology, defense, agriculture and environmental sciences. This active research field studies imaging inverse problems involving the estimation of an unobserved true image from measurements that are noisy, incomplete and resolution-limited. This proposal focuses on an increasingly important and particularly challenging class of imaging inverse problems that, in addition to being ill-posed and ill-conditioned, are further complicated by inaccurate and partial knowledge of the observation system and of the properties of the underlying true image (which are essential to regularise the problem and deliver meaningful estimates). These so-called “semi-blind” and “unsupervised” problems are the focus of significant research efforts across a range of scientific communities, particularly applied analysis, Bayesian statistics, and signal processing, which have recently produced important developments in mathematical theory, methods, models and efficient algorithms.
The proposed research workshop will focus on three specific aspects of imaging with partially unknown models that will be key in future methodology: learning models from observed data, model comparison and selection in the absence of ground truth, and robust inference with approximate models.
We welcome applications to present a poster during a lunchtime poster session, if you are interested in presenting a poster please provide the title and a short abstract when registering.
Plenary speakers are:
Gabriel Peyré (Université Paris-Dauphine)
Silvia Villa (Istituto Italiano di Tecnologia and Massachusetts Institute of Technology)
Yves Wiaux (Heriot-Watt University)
Juan Carlos de los Reyes (Escuela Nacional Politécnica de Quito)
John Aston (University of Cambridge)
Samuli Siltanen (University of Helsinki)
Yoann Altmann (Heriot Watt, UK)
Martin Benning (University of Cambridge, UK)
Natalia Bochkina (University of Edinburgh, UK)
Matthias Ehrhardt (University of Cambridge, UK)
Teresa Klatzer (Graz University of Technology, Austria)
Felix Lucka (University College London, UK)
Marcelo Pereyra (Heriot Watt, UK)
Carola-Bibiane Schönlieb (CCIMI, University of Cambridge, UK)
Luca Calatroni (Ecole Polytechnique, France)
Veronica Corona (University of Cambridge, UK)
Karl Harrison (University of Cambridge, UK)
Eugenie Hunsicker (Loughborough University, UK)
Lukas Lang (University of Cambridge, UK)
Nguyet Minh Mach (University of Helsinki)
Sebastian Neumayer (University of Cambridge, UK)
Shannon Seah (University of Cambridge, UK)
Ferdia Sherry (University of Cambridge, UK)
Megan Wilson (University of Cambridge, UK)
There is a registration fee of 20 pounds. The number of participants is limited, so please make sure that you register early. We apologise in advance to those whom we cannot accommodate. To register please visit the following payment page and to find out more please contact email@example.com.