Daniel Berlyne

I currently work as a Mathematics Researcher at ThinkTank Maths, a role that lies at the intersection of mathematics and data science. I work with technical data from scientific domains (mostly related to space systems and renewable energy) and develop novel mathematical techniques to analyse and model it, as well as implementing solutions for clients in Python.

At ThinkTank Maths, I have worked on the following projects:

• Developing data-driven methods for precise satellite positioning and atmospheric modelling for the OPS-SAT VOLT project (in conjunction with Craft Prospect and the European Space Agency)
• Using machine learning and deep learning techniques to analyse data and develop surrogate models for spacecraft thermal engineering (in conjunction with the European Space Agency)
• Modelling and optimising the production of green e-fuels using renewable wind power, with the goal of decarbonising the Sullom Voe oil terminal in Shetland (in conjunction with Veri Energy and the Net Zero Technology Centre)

Before I joined ThinkTank Maths, I was a Heilbronn Research Fellow at the University of Bristol, working with Mark Hagen. Prior to this, I completed my Ph.D. in Mathematics at the City University of New York; my thesis advisor was Jason Behrstock.

My research was in geometric group theory and low-dimensional topology, utilising tools from algebra, graph theory, combinatorics, and probability. I wrote two Python programs implementing algorithms detailed in my paper on graph braid groups. The code is available on my GitHub page in the ‘graph-braid-splitter’ and ‘graph-braid-presenter’ repositories.