Data Science Seminar
Applications of global and local persistent homology for the shape of biological data
Bernadette Stolz
Applications of global and local persistent homology for the shape of biological data
| When | Wednesday, November 16, 2022, 10:30 AM – 11:45 AM (MT) |
|---|
Abstract
In the first part of this talk, I will showcase how persistent homology can be used to spatially characterise structural abnormality in tumour blood vessel networks. More specifically, I will show that the number of vessel loops and their distribution in these networks change over time when tumours undergo treatment with vascular targeting agents and radiation therapy. In the second part of the talk, I will speak about applications of local persistent homology. I will show how local persistent homology can be used to select landmarks from large and noisy data sets. In contrast to existing methods, this subsampling process is robust to outliers and is developed specifically for persistent homology. I will further introduce a novel method that can detect geometric anomalies, such as intersections or boundaries, in point cloud data sampled from intersecting surfaces. This detection is based on the computation of persistent homology in local annular neighbourhoods around points and is less sensitive to the size of the local neighbourhood and surface curvature than an existing method.
Speaker
Bernadette Stolz
Oxford
Bernadette obtained her DPhil in 2020 from the Mathematical Institute at the University of Oxford. She is now a Postdoctoral Researcher at the Laboratory for Topology and Neuroscience at EPFL and a Visiting Research Fellow at the Mathematical Institute, University of Oxford. Previously she was a Postdoctoral Research Assistant at the Centre for Topological Data Analysis at the University of Oxford. In her research, she develops techniques in topological data analysis (TDA) to study biological data, in particular dynamical networks and spatial data. Her research can be broadly categorised into three main groups: 1) Developing TDA techniques to answer biological questions arising from experimental data. 2) Developing novel data science methods based on TDA. 3) Using TDA in combination with mechanistic models to link form and function in biological systems. Her expertise in TDA is complemented by an MSc in Mathematical Modelling and Scientific Computing (University of Oxford, 2014) and undergraduate degrees in Mathematics (University of Bern, 2012) and Molecular Medicine (University of Göttingen, 2009). Her research has been recognised with the L’Oréal-Unesco For Women in Science Rising Talent Award 2022, the Anile-ECMI Prize for best PhD thesis 2021, and the Mathematical Institute (University of Oxford) 2020 DPhil Thesis Prize.
Tags: biology & genomics
Part of the Data Science Seminar. Something wrong on this page? Edit _data/talks/2022-11-16-bernadette-stolz.toml.