Dr Norma Bargary

Dr Norma Bargary (Call 2)

Contact: norma.bargary@ul.ie

PROCESS Research Theme: Process modelling and integrated process control, Process Analytical Technologies (PAT)

Research Interests:
Dr Bargary's research interests include: 

  • Statistical analysis of time-course/functional data.
  • Smoothing using the mixed effects model.
  • Finite mixture model clustering of time-course data. 
  • Modelling -omics data, e.g. RNA-seq data, mass spectrometry data, ChiP-seq data, etc.
  • Biostatistics.
  • Statistical modelling in biomechanics. 

Short Bio:

I graduated in 2005 from the University of Limerick with a BSc. in Mathematical Sciences and Computing (majoring in Statistics). I subsequently began a PhD at UL in 2005 with Dr. Kevin Hayes, examining the use of the linear mixed effects model in functional data analysis. I graduated in 2008 and then spent a year lecturing statistics in UL. In 2009 I began a postdoctoral position with Professor John Hinde at NUI Galway, developing models to cluster time-course microarray data. In 2011, I was… read more appointed as lecturer in statistics at UCD and began the position in January 2012. This was a joint appointment between the School of Mathematical Sciences and Systems Biology Ireland. I began my current lecturing position at the University of Limerick in September 2013.

My main area of interest is in the statistical modelling of time-course/functional data using the mixed effects model. I have applied these methods to analyse biological data, e.g. RNA-seq, ChIP-seq, mass-spectometry, microarray data and biomechanics data.

Publications:

A list of publications can be viewed on Dr Bargary's research profile with the Mathematics Applications Consortium for Science and Industry (MACSI) site: https://ulsites.ul.ie/macsi/node/44131

 

Qualifications:

  • BSc (Hons)  Mathematical Sciences and Computing, majoring in Statistics, University of Limerick
  • PhD Statistics, University of Limerick

Call 2 Now Open*** Deadline 2 March 2020****

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801165.

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