Matthias Chung, Emory University
Matthias Ehrhardt, University of Bath
Carola Bibiane Schönlieb, University of Cambridge
The rapidly evolving field of data science recognizes the urgent need for novel computational methods to overcome challenges of inference and uncertainty quantification to make informed decisions in big data settings. Emerging fields such as data analytics, machine learning, and uncertainty quantification rely heavily on efficient computational methods for inverse problems.
This workshop will gather researchers from the intersecting fields of inverse problems, uncertainty quantification, data analytics, machine learning, and related areas to discuss novel theory and new methods for big data challenges. A focused multifaceted research group gathered at one location is an excellent opportunity for established and early career researchers. The aim of this workshop is to foster a diverse, open, and friendly environment to advance this field, open new directions, and to stimulate new collaboration.