Associate ProfessorDaniel Tubbenhauer

Project Opportunities

Title: Big Data and Monoidal Categories

Summary of opportunity:

This project aims to generate and collect data associated with monoidal categories, in particular focusing on generating associators and (exotic) quantum invariants. The resulting data will be analyzed using a mix of traditional methods, big data approaches, and deep learning techniques to gain new insights into key open problems in the field. A background in representation theory and computer science is desirable.

---------------Current Research Projects----------

Currently my research focusses on various aspects involving 2-representation theory:

1. The abstract theory: allow infinite 2-categories and work in finite characteristic.
2. Low-dimensional topology: link homologies and 2-representations of braid groups.
3. Cryptography: diagram categories and linear attacks.
4. Machine learning via representation theory.
5. Representation theory of KLRW algebras and alike.
6. Webs in various forms.