Peter T. Kim

Professor College of Physical & Engineering Science Department of Mathematics & Statistics Guelph, Ontario pkim@uoguelph.ca Office: (519) 824-4120 ext. 52155
(519) 824-4120 ext. 58165

Bio/Research

My recent research interests have been in Bioinformatics and Biostatistics, mainly in a clinical setting. I also hold a faculty appointment at the Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University.

In Biostatistics, interest comes in the for...


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Bio/Research

My recent research interests have been in Bioinformatics and Biostatistics, mainly in a clinical setting. I also hold a faculty appointment at the Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University.

In Biostatistics, interest comes in the form of clinical trials and longitudinal data analysis. This is reflected in my publications where the works come from data obtained in a clinical setting. Also in a clinical setting, there is much interest in Clostridium difficile.

The Bioinformatic interest comes from sequencing the gut microbiome in both a human and veterinarian setting. In collaboration with the Department of Pathobiology, we are using high throughput sequencing to try and understand the changes and/or differences in the gut microbiome.

I am part of a research team who have obtained significant funding to further understand gastrointestinal diseases. In particular, we have obtained a Collaborative Health Research Projects (CHRP) through CIHR-NSERC. Additional funding comes from AHSC, Equine Guelph and NSERC

Statistical function estimation on Riemannian manifolds continue to be of interest. We obtained major results in minimax estimation on the space of positive definite

symmetric matrices which was published in the Annals of Statistics, one of the top journals in statistics.

A related but different avenue of interest is in computational algebraic topology where the idea is the development of a statistical Morse theory based on the level sets of an estimated function from some underlying manifold. By statistically calculating the persistent homology, one can recover topological information whereby local clustering of data can be homologically recovered. This represents the furthest achievement thus far and appeared in the Journal of the American Statistical Association, another top journal in statistics.


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Lori Bona Hunt
l.hunt@exec.uoguelph.ca
519-824-4120 ext. 53338

Kevin Gonsalves
kgonsalves@uoguelph.ca
519-824-4120 ext. 56982