Dr Michelle Carey is a Lecturer/Assistant Professor in Statistics at University College Dublin. She is a graduate of the University of Limerick (UL) with a BSc in Financial Mathematics and received her PhD in Statistics from UL in 2012.
In 2011, she joined the Department of Accounting and Finance, Kemmy Business School, UL as a Lecturer in Finance. In 2013, she started a postdoctoral fellowship in biostatistics at the Department of Biostatistics and Computational Biology in the University of Rochester School of Medicine and Dentistry, Rochester, New York, USA. During her postdoc, she researched the evolution of biological systems in terms of differential equations under the direction of Prof Hulin Wu, a prominent expert in biostatistics and data science. In 2015, she started a postdoctoral fellowship in statistics at the Department of Mathematics and Statistics, McGill University, Montreal, Canada. During her postdoc, she researched statistical methods for the analysis of functional data under the direction of the internationally acclaimed founder of functional data analysis, Prof James O. Ramsay, and a leading authority in the field of multivariate analysis, nonparametric statistics and extreme-value theory, Prof Christian Genest.
Michelle's research combines statistics and applied mathematics. It involves the development of advanced statistical and numerical methods for the analysis of data in climatology, medicine, finance and agriculture.
Applied Mathematics: Differential Equation Models
Differential equation models are theoretically built mechanistic models for complex systems. A single differential equation model can describe a wide variety of behaviours including oscillations, steady states and exponential growth and decay, with few but readily interpretable parameters. Consequently, differential equation models are routinely used in describing chemical reaction dynamics, predator-prey interactions, heat transfer, economic growth, epidemiological outbreaks, climate and weather prediction, gene regulatory pathways, etc.
Statistics: Functional Data Analysis
Functional data analysis (FDA) is a branch of statistics that analyzes data evolving over one-or multi-dimensional domains, providing information for example about curves (denoting how a process varies over one dimension (i.e time, space, or frequency) or surfaces denoting how a process changes over two or more dimensions (i.e latitude, longitude and time).
In classical multivariate statistics, we take multiple measurements for each subject, e.g. height, weight, and age. Functional data is multivariate data with an ordering on the dimensions, for example, measurements for each subjects height, weight, and age measured at various points in time. The key assumption is that the dependence between the measurements is a smooth process (each subjects' height today is similar to their height tomorrow).
FDA allows us to acquire:
- Representations of the distribution of the functions that is their mean, variation, and covariation.
- Relationships of functional data to covariates, responses, or other functions.
- Relationships between derivatives of functions (differential equations).
PhD Funding Opportunities
SFI Centre for Research Training in Foundations of Data Science.
I am a named supervisor in SFI Centre for Research Training in Foundations of Data Science. This large-scale collaborative initiative between the University of Limerick (UL), University College Dublin (UCD) and Maynooth University (MU) will train a cohort of PhD students with a world-class foundational understanding in the horizontal themes of Applied Mathematics, Statistics, and Machine Learning. These fundamental themes will be fused by applying them to real-world challenges in vertical themes, including Data Analytics, Privacy & Security, Smart Manufacturing, Networks, and Health & Wellbeing. Students will have work placements in relevant industries to develop cognisance of industry requirements, essential transversal skills, diversity and research impact. Academic placements in internationally renowned collaborating institutions will be available, and students will benefit from exposure to Ireland's world-class research universities.
An application portal will open on the website www.data-science.ie on the 13th of January and will accept applications from interested parties until the 8th of March, with the 2020 cohort commencing in September.
SFI Centre for Research Training in Genomics Data Science
I am a supervisor in SFI Centre for Research Training in Genomics Data Science. The SFI Centre for Research Training in Genomics Data Science is a PhD training programme, linking together approx. Ninety genomics data science group leaders based at six Irish institutions. Expertise within the group spans from statistical modelling and machine learning to the full range of application of genomics. This Research Centre is focussed on 7 Research Themes:
Recruitment for the 2020 cohort of students is now open. Students recruited to the programme will start in September 2020. An application portal is accessible on the website https://genomicsdatascience.ie/ and will accept applications from interested parties until the 12th January 2020, with the 2020 cohort commencing in September.
PhD in Statistics on Data Analytics & Artificial Intelligence for Animal Health funded by the VistaMilk SFI Research Centre
Co-supervisor: Dr Emer Kennedy, Senior Research Officer (Teagasc)
Lead supervisor: Dr Michelle Carey (Statistics UCD)
Applications are invited for a PhD in Statistics studentship in the School of Mathematics and Statistics, University College Dublin, Ireland. The successful applicant will work on a project entitled ‘Data Analytics & Artificial Intelligence for Animal Health’. The project involves collaboration between Dr Emer Kennedy, Senior Research Officer in Teagasc and Dr Michelle Carey, UCD School of Mathematics and Statistics. The position is funded through the VistaMilk SFI Research Centre (https://vistamilk.ie/).
This PhD scholarship holder will work at the interface between Data-Analytics/Artificial Intelligence and precision pasture-based dairying/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-analytics/artificial Intelligence models for identifying and suppressing diseases in dairy herds, such as mastitis, by utilising the data generated from on-farm sensors.
University College Dublin is Ireland’s premier research university and the research-active Statistics group is one of the highest-ranked in Ireland. As UCD is a member institution, the successful applicant will be eligible to attend the beneficial APTS (www.apts.ac.uk) statistics courses during their first year. University College Dublin is a campus-based university with excellent facilities, only a few kilometres from Dublin city centre.
A candidate must have reached a minimum of an upper second class degree or equivalent in a relevant honours Bachelor’s degree. In a significant number of cases, a Master’s degree will also be required. All applicants are required to demonstrate a high level of competence in the English language. Applicants who have not undertaken their undergraduate degree / Master’s degree through English must provide evidence of their English proficiency by achieving a minimum standard in a recognised English language test such as IELTS. Applicants should have experience or an interest in functional data analysis, time-series modelling, and multivariate analysis. Interest in computational statistics and statistical programming will also be of benefit. Applicants should have a numerate undergraduate degree, and an MSc in Statistics is highly desirable.
The starting date is May 1st or Sep 1st 2020. The fellowship covers PhD fees (fully covered for EU students and partially covered for non-EU students) and attracts a tax-free stipend averaging €18,000 per annum for four years. An application consisting of a cover letter, curriculum vitae (including the names and contact details of three referees) and scanned copies of relevant academic transcripts should be sent to Dr Carey (email@example.com) by 5 pm March 30th 2020. Late applications will not be considered.
EPSRC Centre for Doctoral Training in Statistics and Operational Research in Partnership with Industry (STOR-i)
I am a joint supervisor in EPSRC Centre for Doctoral Training in Statistics and Operational Research in Partnership with Industry (STOR-i) with Dr Juhyun Park. The EPSRC Centre for Doctoral Training in Statistics and Operational Research in Partnership with Industry (STOR-i) based at Lancaster University, has 14 fully-funded PhD studentships available to start in October 2020.
Established in 2010, STOR-i has developed an international reputation for its unique training programme, using industrial challenge as the catalyst for methodological advance. Our cohort-based, 4-year scheme offers a distinctive PhD training experience, which provides an opportunity to:
- be part of an exciting community of like-minded peers;
- work directly with leading industry partners;
- be mentored by internationally recognised researcher leaders;
- make a significant scientific and industrial impact with your research;
- be part of international research teams.
These fully-funded studentships are available for UK and EU applicants to start in October 2020. Studentships include a generous tax-free stipend (£17,009 in year 1), fees and an allowance to support research-related activities. Stipends rise up to £20,009 per year on successful progression to PhD at the end of the first year of the programme for students undertaking industry-funded PhD projects - click here for more details.
Early applications are encouraged, as we will be interviewing candidates on an ongoing basis. To find out more please visit, www.stori.lancs.ac.uk/apply.
Exceptional overseas applicants may be considered for a limited number of funded places.