Bernadette Stolz-Pretzer (2013) wins awards for her DPhil thesis

Congratulations to Bernadette Stolz-Pretzer, a former Lincoln graduate student who has been awarded the Mathematical Institute DPhil Thesis Prize 2020 and the 2021 Anile-ECMI Prize for her thesis, 'Global and local persistent homology for the shape and classification of biological data'. The Anile-ECMI Prize is given to a young researcher for an excellent PhD thesis in industrial mathematics successfully submitted at a European University, and includes an invitation to give a talk at the ECMI 2021 Conference.

"I feel greatly honoured and humbled for my research to be recognised in this way. It’s been a very exciting few years to be working in topological data analysis and being part of making the methods more known and accessible for applications. I was very lucky to be supported on this path by a truly fantastic group of academic and industrial supervisors and collaborators. I am very thankful for the wonderful environment the Lincoln community provided for my DPhil." - Bernadette Stolz

Bernadette’s research lies at the intersection of pure mathematics, biology, and data science. In particular, she focuses on applying ideas from topology, an area of mathematics that studies shapes, to biological data so we can gain insights into complex biological processes. In the past few years, advances in imaging techniques have led to an unprecedented volume of biological data. To fully capture and quantify the information in such data, new methods are required. In her DPhil, Bernadette investigated the use of topological data analysis to quantify the unique features of tumour blood vessel networks and monitor changes of these network features in response to different treatments. She further applied similar techniques to study networks from neuroscience experiments. As computations of topological properties in data can take a very long time (up to years on some of Oxford's most powerful computers!), Bernadette researched what we can learn from local calculations in small neighbourhoods of data points and developed new mathematical methods. Her results indicate that such local computations can be used to select landmarks, i.e. a small representative subsample of the data, from large and noisy data sets. She further applied related ideas to identify data points close to intersections in non-manifold data, for example data sampled from intersecting surfaces. She applied the latter technique to study the 24-dimensional space of a hydrocarbon called cyclo-octane and its demonstrated advantages over more traditional methods. This work has recently been published in the renowned journal PNAS (Proceedings of the National Academy of Sciences).