About

           I am currently a Banting Postdoctoral Fellow in the department of Mathematics and Statistics at McMaster University, working with Dr. Paul D. McNicholas. My research interests lie in the area of clustering and classification, specifically model-based methods. My current research is in the area of clustering and classification using matrix variate distributions.

Degrees

• Doctor of Philosophy, McMaster University (2020)
• Master of Science, McMaster University (2017)
• Bachelor of Science, McMaster University (2015)

Awards

• Banting Postdoctoral Fellowship (2020)
• James Stewart Fellowship (2019)
• Vanier Canada Graduate Scholarship (2016-2019)
• MacDATA Fellowship (2018)
• Student Delegate to the Commonwealth Science Conference, Singapore (2017)

Publications

• Gallaugher, M.P.B. and McNicholas, P.D. (2020), 'Parsimonious mixtures of matrix variate bilinear factor analyzers'. In T. Imaizumi et al., editors, Advanced Studies in Behaviormetrics and Data Science. Springer, Singapore.
  
• Gallaugher, M.P.B. and McNicholas, P.D. (2019), 'Mixtures of skewed matrix variate bilinear factor analyzers', Advances in Data Analysis and Classification [doi]
  
• Gallaugher, M.P.B. and McNicholas, P.D. (2019), 'On fractionally-supervised classification: Weight selection and extension to the multivariate t-distribution', Journal of Classification 36, 232-265. [doi] [preprint]
  
• Gallaugher, M.P.B. and McNicholas, P.D. (2019), 'Three skewed matrix variate distributions', Statistics and Probability Letters 145, 103-109. [doi] [preprint]
• Gallaugher, M.P.B. and McNicholas, P.D. (2018), 'Mixtures of matrix variate bilinear factor analyzers', JSM 2018 Proceedings. Alexandria, VA: American Statistical Association. 
• Morton, R.W., Sato, K., Gallaugher, M.P.B., Oikawa, S.Y., McNicholas, P.D., Fujita, S. and Phillips, S.M. (2018), 'Muscle androgen receptor content but not systemic hormones is associated with resistance training-induced skeletal muscle hypertrophy in healthy, young men', Frontiers in Physiology 9, 1373. [doi]
• Gallaugher, M.P.B. and McNicholas, P.D. (2018), 'Finite mixtures of skewed matrix variate distributions', Pattern Recognition 80, 83-93. [doi][preprint]
  
• Gallaugher, M.P.B. and McNicholas, P.D. (2017), Discussion of 'Random-projection ensemble classification’ by Cannings and Samworth, Journal of the Royal Statistical Society: Series B 79(4), 1011-1012. [doi]
• Gallaugher, M.P.B. and McNicholas, P.D. (2017), 'A matrix variate skew-t distribution', Stat 6(1), 160-170. [doi] [preprint]
• Gallaugher M., Canty A.J., Paterson A.D. (2016), `Factors associated with heterogeneity in microarray gene expression in peripheral blood mononuclear cells from large pedigrees.',BMC Proceedings 10(7), 91-95.
• Buzano, M., Dancer, A.S., Gallaugher, M., Wang, M. (2015), `Non-Kähler expanding Ricci solitons, Einstein metrics, and exotic cone structures', Pacific Journal of Mathematics, 273(2) 369-394.

PrePrints

• Dang, U.J., Gallaugher, M.P.B., Browne, R.P., McNicholas, P.D. (2019), 'Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions', arXiv preprint arXiv:1907.01938.
• Pocuca, N., Gallaugher, M.P.B., Clark, K.M., McNicholas, P.D. (2019), 'Assessing and visualizing matrix variate normality', arXiv preprint arXiv:1910.02859.
• Gallaugher, M.P.B., Tang, Y., McNicholas, P.D. (2019), 'Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions', arXiv preprint arXiv:1903.05054.
• Gallaugher, M.P.B., Biernacki, C., McNicholas, P.D. (2018), 'Relaxing the identically distributed assumption in Gaussian co-clustering for high dimensional data', arXiv preprint arXiv: 1808.08366
  

Presentations

• “Advances in clustering and classification.”, Statistics Seminar, McMaster University, Hamilton, Canada , November 2019, (Invited)
  
• “Relaxing the identically distributed assumption in Gaussian co-clustering for high-dimensional data.”, Conference on Data Science, Fields Institute, Toronto, Canada , November 2019, (Invited)
  
• “Skewed distributions or transformations? Incorporating skewness in a cluster analysis.”, 12th Scientific Meet-
  ing of The Classification and Data Analysis Group, Cassino Italy, September 2019, (Invited)
  
• “Skewed distributions or transformations? Accounting for skewness in cluster analysis.”, 16th Conference of
  The International Federation of Classification Societies, Thessaloniki, Greece, August 2019, (Invited)
  
• “Skewed distributions or transformations? Handling skewed data in a cluster analysis.”, 16th The Classification Society Annual Meeting, Edmonton, Canada, June 2019
  
•  "Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers", ERCIM 2018, Pisa Italy, December 2018, (Invited)
  
• "Finite mixtures of skewed matrix variate distributions", The Classification Society Annual Meeting, Stoney Brook, Long Island, New York, June 2018.
• "Matrix variate mixtures", MTO Colloquium, Tilburg, The Netherlands, May 2018. (Invited)
• "Matrix variate mixtures", Inria Modal Seminar, Lille, France, April 2018. (Invited)
• "Clustering and semi-supervised classification for clickstream data via mixture models",The Classification Society Annual Meeting, Santa Cruz, California, June 2017.
• "Clustering and classication of clickstream data with applications in anti-terrorism", The Commonwealth Science Conference, Singapore, June 2017. (Invited)
• "Clustering clickstream data using a mixture of continuous time Markov models",The International Conference on Statistical Distributions and Applications, Niagara Falls, Ontario, October 2016. (Invited)
• "Extending fractionally supervised classification to non-Gaussian mixture models", The Classification Society Annual Meeting, St. Louis, Missouri, June 2016.
• "Extending fractionally supervised classification to non-Gaussian mixture models", The Statistical Society of Canada Annual Meeting, St. Catherines, Canada, May 2016.