EPSRC Reference: 
EP/R018472/1 
Title: 
Application driven Topological Data Analysis 
Principal Investigator: 
Tillmann, Professor U 
Other Investigators: 
Cooper, Professor A 
Potapov, Professor I 
Harrington, Professor H 
Dlotko, Dr P 
Kurlin, Dr V 
Byrne, Professor H 
Reinert, Professor G 
Spirakis, Professor P 
BeguerisseDiaz, Dr M 
Giansiracusa, Professor J 
Grindrod, Professor P 


Researcher CoInvestigators: 

Project Partners: 

Department: 
Mathematical Institute 
Organisation: 
University of Oxford 
Scheme: 
Standard Research 
Starts: 
01 September 2018 
Ends: 
31 August 2024 
Value (£): 
2,847,111

EPSRC Research Topic Classifications: 
Algebra & Geometry 
Statistics & Appl. Probability 

EPSRC Industrial Sector Classifications: 
Information Technologies 
R&D 

Related Grants: 

Panel History: 

Summary on Grant Application Form 
Modern science and technology generates data at an unprecedented rate. A major challenge is that this data is often complex, high dimensional, may include temporal and/or spatial information. The "shape" of the data can be important but it is difficult to extract and quantify it using standard machine learning or statistical techniques. For example, an image of blood vessels near a tumor looks very different than an image of healthy blood
vessels; statistics alone cannot quantify this shape because it is the shape that matters. The focus of this proposal is to study the shape of data, through the development of new mathematics and algorithms, and build on existing data science techniques in order to obtain and interpret the shape of data. A theoretical field of mathematics that enables the study of shapes is topology. The ability to compute the shape (its topology) of complicated shapes is only possible with advanced mathematics and algorithms. The field known as topological data analysis (TDA), enables one to use topology to study the shape of data, such as loops in a blood vessel network. In particular, an algorithm within TDA known as persistent homology, provides a topological summary of the shape of the data (e.g., features such as holes) at multiple scales. A key success of persistent homology is the ability to provide robust results, even if the data are noisy. There are theoretical and computational challenges in the application of these algorithms to large scale, realworld data.
The aim of this project is to build on current persistent homology tools, extending it theoretically, computationally, and adapting it for practical applications. Our core team is composed of experts in pure and applied mathematicians, computer scientists, and statisticians whose combined expertise covers cutting edge pure mathematics, mathematical modeling, algorithm design and data analysis. This core team will work closely with our collaborators in a range of scientific and industrial domains. Some of the application challenges we have set out include:
Can we detect a tumor by looking at the shape of images of blood vessels? Can we design new materials by looking at the shape of molecules using topology? How can we design such molecules? Can we detect anomalies in security data? And importantly, how can we accelerate algorithms to obtain topological characteristics of data in real time?

Key Findings 
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Potential use in nonacademic contexts 
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Impacts 
Description 
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Summary 

Date Materialised 


Sectors submitted by the Researcher 
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Project URL: 

Further Information: 

Organisation Website: 
http://www.ox.ac.uk 