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Mr. Kevin Brosnan


I am passionate about developing new statistical techniques to address real life problems. I have completed a B.Sc in Mathematical Sciences at the University of Limerick, with a major in Statistics.
I am currently pursuing a Ph.D. at the University of Limerick with my thesis titled "Data Analysis of Flow Cytometry data with inclusion of key biological knowledge". Flow Cytometry is used across industries such as medical diagnostics and dairy sciences, however the current statistical methods do not compliment the technology used. Developing these improved statistical methods is the aim of my research.
I also have experience in Management Consultancy (Accenture), Banking (AIB) and developing analytic tools for multinational companies (Accenture).

Research Interests

My main research focus is the application of novel statistical approaches to real-world problems. These statistical methods provide a way of moving beyond the industry standard in the chosen application areas.

Statistical Modelling of Flow Cytometry Data

Flow cytometry is a technology that simultaneously measures and analyses multiple physical

and chemical characteristics of single cells as they flow in a stream through a beam of laser light. This technology has become an emerging state-of-the-art device in microbiology and dairy science, and is also used extensively in medical diagnostics. Unfortunately, the lack of a robust statistical analysis toolbox for flow cytometry has restricted the deployment of this world-leading sensor technology. The gating, identification of homogeneous cell populations, and tagging, identification of correlations between some characteristics of the identified cell populations and product characteristics, of the complex data sets produced are performed using experts opinion rather than by employing a unified statistical framework. This research expands beyond the current standard for flow cytometry by proposing a non-expert driven framework for the gating and tagging of this high-dimensional data.

Statistical Applications in Elite Athletics
Sprint Starts Response Time of Elite Athletes:
A study in collaboration with Prof. Drew Harrison, Department of Physical Education and Sports Science, to evaluate if the 100 ms disqualification limit for detecting false starts in international competitions is still valid. A statistical modelling approach was utilised to identify a sex difference in response times of elite athletes and a revised threshold was proposed.

Professional Activities


  • 2018 University of Limerick - PhD
  • 2014 University of Limerick - B.Sc. (Applied Mathematics)


  • 2017 Young Statisticians Section of the Royal Statistical Society,


  • 2016 Member, Irish Statistical Association
  • 2015 Student Member, Royal Statistical Society


  • 2016 - DatSci Data Science Student of the Year
  • 2014 - IRC Postgraduate Scholarship
  • 2014 - Gold Medal Recipient
  • 2014 - Silver Medal Recipient
  • 2013 - 2013 Hamilton Prize in Mathematics


  • 2014 Allied Irish Banks - Bank Official
  • 2013 Accenture Analytics, Dublin - Intern Analyst


  • Mathematics Co-ordinator of the President's Volunteer Programme (PVP) at the University of Limerick for academic year 2014/2015 and 2015/2016.
    The PVP provides free maths and science tuition to junior and leaving certificate students from disadvantaged schools across Limerick city and county.


Peer Reviewed Journals


Effects of false start disqualification rules on response-times of elite-standard sprinters
Brosnan, K.C., Hayes, K. and Harrison, A.J.
(2017) Effects of false start disqualification rules on response-times of elite-standard sprinters
In Journal Of Sports Sciences; pp. 929-935
DOI: 10.1080/02640414.2016.1201213