After co-leading the "Signatures for Images" project at CAS, mathematician Fabian Harang is now tackling climate challenges in the grocery industry - connecting basic research and business interests.
2023/2024
Signatures for Images
Signatures for Images
Mathematics
Principal investigators
Kurusch Ebrahimi-Fard
Professor
Norwegian University of Science and Technology (NTNU)
Year at CAS
Research Area
Mathematics
Abstract
An expert team with diverse mathematical backgrounds will build the mathematical foundations for image classification using iterated-integrals signature in the context of rough paths theory. This represents a significant mathematical challenge at the intersection of algebra, stochastic analysis, and geometry. Involving researchers with expertise in machine learning, we will explore signatures of images with a view towards real world applications. Related questions regarding statistical properties of signatures of images will be investigated and answered. The project will provide a platform for interdisciplinary research, reaching from pure mathematics into data science, and aiming at preparing applications for RCN and ERC projects.
Fellows
Christa Cuchiero
Professor
University of Vienna
Year at CAS
Research Area
Mathematics
/
Stochastic Analysis
Fride Straum
PhD Candidate
Norwegian University of Science and Technology (NTNU)
Year at CAS
News
BI Norwegian Business School has launched "AMOR – Center for Applied Mathematics and Operations Research," a new research center led by former CAS PI and mathematician Fabian Harang. The center has been made possible through funding from Reitan Retail and Rema 1000.
Kurusch Ebrahimi-Fard, Professor at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) and Fabian Nøst Harang, Professor in Mathematics at BI Norwegian Business, is co-leading the CAS project "Signatures for Images" this academic year. This unique initiative aims to build mathematical foundations for image classification, leveraging iterated-integrals signature in the context of rough paths theory. It’s a venture at the intersection of algebra, stochastic analysis, and geometry, involving experts in machine learning to explore real-world applications and investigate the statistical properties of image signatures.