Interns 2013

Below you can find details of the summer 2013 interns including a description of their research project. Click here to view the weekly blog written by 2013 interns.

Martin Andla
University of York, MMath Mathematics (2010-present)
Supervisor: George Foulds

Statistical Modelling in Sports

To aid the application of betting and investment strategies an edge must be sought over the market. Simulation modelling is a crucial part of this process, providing evidence to support real-world data analysis and professional conjecture. This project will introduce the student to the use of statistical modelling in the prediction of sports results and allow them to adapt a well-known model using their findings from freely available real world data.

View Martin's presentation and poster.

Jenny August
University of Edinburgh, BSc Mathematics (2010-present)
Supervisor: Ben Pickering

Detecting Changepoints in Multivariate Data Series

Data collection is a huge component of the workings of any modern organisation. There are many examples of situations where data is collected from multiple sources which may be related in some way, for example, the stock prices of multiple companies in the same industrial sector. While the nature of these data values may stay fairly constant over time, occasionally some event may occur which causes a sudden change in the values being recorded at all sources, for example, in financial data, there may be a stock market crash. The times at which such changes occur are known as multivariate changepoints. This project will explore the effectiveness of current multivariate changepoint methods.

View Jenny's presentation and poster.

Thomas Berrett
University of Cambridge, BA Mathematics (2010-2014)
Supervisor: David Hofmeyr

Learning in Dynamic Environments

Machine learning is a field of artificial intelligence focused on developing algorithms which allow computers to evolve and improve their behaviour as a result of empirical data. In the context of this project, this refers to the construction of a data driven model to aid in a predefined task. The task might be something basic like making predictions based on a simple regression model, or it might be highly complex like describing intricate biological systems. This project offers a variety of possibilities due to the lack of specificity in online learning, and there is considerable flexibility for its direction depending on the student’s preference. 

View Thomas's presentation and poster.

Simon Crawford
University of Bath, MMath Mathematics (2010-present)
Supervisor: James Edwards

Bayes Sequential Decision Problems

Many important decisions have to be made under uncertainty because information that is relevant to the problem is missing or is only known imperfectly. Often, these decisions are not taken in isolation but in a sequence. New information that becomes available as a result of our actions can then be used to make better decisions in the future.  However, the actions that give the best short term results may not be the same actions that give the most information. This presents a trade-off between taking the actions that are best in the short term and the need to learn for better long term results. This project will explore a number of statistical theories for dealing with decision problems followed by testing and selecting optimal methods.

View Simon's presentation and poster.

Oliver Hatfield
University of Durham, MMath Mathematics (2010-2014)
Supervisor: Rob Maidstone

Multiple Changepoint Detection in Non-Trivial Models

Time series data sometimes experiences abrupt changes in structure. These changes are called changepoints. To model the data effectively these changepoints need to be detected and subsequently built into the model. Changepoints occur in a variety of real world situations, for example when analysing human genome data the average DNA copy value is usually around the same level, however occasionally sudden changes away from this level occur. These sudden changes in average DNA level often relate to tumourous cells and therefore the detection of these changes is critical for classifying the tumour type and progression. This project will introduce the student to changepoint models and involve programming these models using statistical computing software.

View Oliver's presentation and poster.

Lucy Morgan
Lancaster University, BSc Mathematics (2011-2014)
Supervisor: Rhian Davies

Background Subtraction: Methods for Video Analysis

Surveillance cameras have become ubiquitous in many countries, collecting a huge amount of data, most of which is stored and never analysed. Converting this data into useful information can be problematic, particularly as large companies often use many cameras simultaneously. Often it is of interest to the user to detect anomalies in video footage, for example a person placing an item in their bag instead of their shopping trolley. In order to detect such anomalies, we first need to separate the foreground and the background of a video. One popular method for splitting the foreground from the background is background subtraction. The aim of this project is to investigate the effectiveness of different algorithms for background subtraction under a number of real and challenging scenarios. 

View Lucy's presentation and poster.

Ciara Pike-Burke
University of Manchester, BSc Mathematics with Spanish (2010-2014)
Supervisor: Simon Taylor

Evaluating the Structure of the Excitability Curve of Motor Neurons

Scientists in the field of neuromuscular research are interested in understanding the structures and processes involved in operating a working muscle. The fundamental component to this process is a motor unit: consisting of a single motor neuron and a collection of muscle fibres that it governs. Evaluating the number of motor units that form a working muscle is very important in understanding the effects of various neuro-degenerative disorders and also in assessing the effectiveness of proposed treatments. The aim of this project is to analyse data from the stimulation of a single motor unit using importance sampling and Bayesian statistics. 

View Ciara's presentation and poster.

Michelle Pinharry
University of Bath, BSc Mathematics (2011-2014)
Supervisors: Pedro Crespo del Granado and Franklin Djeumou Fomeni

The Unit Commitment Problem and Wind Energy

The UK’s wind renewable resources share in the grid energy generation mix is expected to be around 20-30% by 2020. Wind generation, however, creates new planning challenges to maintain a stable and reliable supply-demand balance. Since wind generation fluctuates independently from energy demand, this creates a disturbance for the short term generation planning and scheduling of other generation units (such as gas or coal power plants). This brings a new degree of uncertainty on stabilizing the power network equilibrium between supply and demand in real time. This project will use optimisation modelling to answer the question what is the optimal cost-effective mix of energy units needed to achieve carbon reduction targets whilst also coping with high wind input? 

View Michelle's presentation and poster.

Benjamin Pring
University of Bath, BSc Mathematics (2010-2013)
Supervisor: Tom Flowerdew

Betting Markets and Strategies

Markets come in many forms. From buying and selling livestock to trading complex financial derivatives the key to making long-term profits is to establish an edge on the market. Once an edge has been established, the question is how can wealth be optimised? This project will investigate ways in which existing theory can be adapted to fit into sports betting markets, and ways in which underlying assumptions can be removed to allow the theory to become more general. 

View Benjamin's presentation and poster.

Emma Simpson
University of Durham, MMath Mathematics (2010-2014)
Supervisor: Ye Liu

A study of the air quality of major cities in China

The air quality in some major cities in China has long suffered from the rapid industrialisation and increasing vehicle usage. With the help of social network and media coverage, this issue has gradually come to the concerns of the government as well as the general public. This research project will aim to gain some insight into the air pollution problem in China using classical statistical techniques such as time series analysis and extreme value theory.

View Emma's presentation and poster.

Kathryn Turnbull
University of Durham, MMath Mathematics (2011-present)
Supervisor: Hugo Winter

Modelling droughts with extreme value theory

Droughts are large scale climatic phenomena that can lead to social and economic damages. In Africa, periods of drought can lead to food instability and large death tolls as well as having a knock-on effect on the economies of major aid providers. In the UK, a drought could cause reservoirs to run low and lead to government legislature such as hose-pipe bans seen over the last few summers. It is of great concern to governments and industry where and when these events may occur and also whether their occurrences will differ in the future with anticipated global climate change. Using standard statistical techniques for rare events will potentially result in badly fitting models and worse, to misleading policies. With such rare and sparse data a more reliable approach is needed; this is called extreme value theory. This project will introduce the student to extreme value theory and its applications for drought data. 

View Kathryn's presentation and poster.

Christina Wright
University of Durham, MMath Mathematics (2010-2014)
Supervisor: Emma Ross

Resource Allocation in Service Industries

The effective allocation of resources to meet demand is an essential consideration of any company hoping to survive in a competitive market. This and many other important decision problems can be formulated as a well-known combinatorial optimisation problem called the knapsack problem. It forms a basis from which to study such decision problems, but we quickly run into difficulty when the complexity and scale of real problems faced in industry are incorporated. This project will allow the student to investigate the impact of uncertainty in resource allocation problems by introducing them to linear programming. 

View Christina's presentation and poster.