We are based in the School of Mathematics and Statistics in the College of Science at University College Dublin.
Current PhD Students
Sajal Kaur Minhas
Mini Project: A functional data analysis of torsional deformities in children with cerebral palsy
The diversity of gait movement observed in children with Cerebral Palsy has led to repeated efforts to develop a valid and reliable gait classification system to assist in the diagnostic process and clinical decision making and the communication of a child’s condition between clinicians. This project looks at using functional data analysis techniques to classify walking patterns into groups based on features of the time series representing the movements in the children's pelvis in three dimensions recorded by optoelectronic devices.
PhD Project: Functional Data Analysis with Applications to High-Frequency 3D Imaging
The project develops statistical methods for complex high-dimensional data using techniques from functional data analysis (FDA). The research is divided into two areas: extending penalized regression methods in order to compute surfaces from 3D noisy data and extending functional regression in order to explain the relationship between the surface and many covariates.
This work is motivated by the ADAPT (Anthropology, DNA, and the Appearance and Perception of Traits) project, we will analyse high-resolution 3D facial images (as depicted in Figure 1), with data consisting of thousands of subjects, thousands of measurements per face, and hundreds of thousands of genetic markers. Figure 1 shows the 3D facial structure of one person's face. The goal of this project is to uncover the genetic architecture of the human face and to better understand the ancestry of different facial features.
Mini Project: Data Analytics & Artificial Intelligence for Animal Health
This project will provide diagnostic options to support sustainable control of priority diseases in dairy herds. In particular, it will develop a suite of data analysis and artificial intelligence models for identifying and suppressing diseases in dairy herds, such as mastitis, by utilizing the data generated from on-farm sensors.
PhD Project: Fragmented Functional Data Analysis for wired and wireless sensors
With the rise of the Internet of Things (IoT), industries are awash in petabytes of Big Data from an increasing array of wired and wireless sensors. The sensors are continuously monitoring and reporting on essential information. For example in the context of animal health, we have on-farm sensors reporting the composition of the cows' milk, yield, protein, fat, lactose, somatic cell count, etc. We take advantage of this wealth of data by creating models to unlock the wisdom embedded within to predict disease, reduce maintenance costs, anticipate equipment failures, etc.
Sensors are prone to missing segments (the sensor is temporarily broken, a sampling site is inaccessible, or data
values are intentionally suppressed to protect confidentiality). Functional data analysis provides the required statistical methods to deal with large-scale and complex data by assuming that data are continuous functions, e.g., realizations of a continuous process (curves) or continuous random field (surfaces) and that each curve or surface is considered as a single observation. We propose a new methodology that aims to recover the missing parts of a function given the observed parts. We then consider approaches for the classification of the fragmented functional data.
Some Former MSc Students
- Bronagh McCann. Prjoect: A shiny app for gait classification that can determine the severity of Cerebral Palsy in children.
- Jingfeng Zhu. Project: Forecasting major currency exchange rates
- Uche Mbaka. Project: Dynamic Analysis of Milk Yield Time Series: A Complex Network Approach
- Sean Mc Mahon. Project: What factors or farm practices affect the susceptibility of dairy cows to sub-clinical vs clinical mastitis?
- Shubbham Gupta. Project: What factors or farm practices affect the time between calving and developing mastitis?
- Akanseoluwa Stephen Adegoke. Project: Exchange Rate Predictability
- Alessandro Franchini. Project: Modelling oceanographic measurements and streams via spatial regression with differential penalization
- Clelia Bambini. Project: Spatial Regression Models with Differential Regularization: problems and solutions in Big Data settings
- Kevin Gallagher. Project: Using Machine Learning methods to analyse the metabolic profiles of professional athletes
- Mark Dignam. Project: Minimising Errors in Wind Generation Forecasts Using Functional Regression
- Prajwal Shetty. Project: A Shiny App for Probabilistic Wind Speed Forecasting using Functional Data Analysis
- Edward Donlon. Project: Uncertainty Quantication for Partial Differential Equations with Applications in Geospatial Data Analysis