I am a Data Scientist with Tesera Systems.
I am also an Adjunct Professor in the Department of Statistics at the University of Manitoba.
I received a PhD in Biostatistics from McGill University in 2019 while working under the supervision of Dr. Celia Greenwood and Dr. Aurélie Labbe. The title of my PhD thesis is Dimension Reduction and High-Dimensional Data: Estimation and Inference with Application to Genomics and Neuroimaging.
In a previous life, I also received an MSc in Mathematics from McGill University. My thesis work was in the field of arithmetic geometry, and it explored a “relative fundamental lemma” that connects distinguished representations on the unitary group of rank 4 with those on general linear groups. It was written under the supervision of Dr. Jayce R. Getz, who is now at Duke University.
Finally, from 2016 to 2019, I was also a senior biostatistician with the Saskatchewan Health Authority in Saskatoon, SK. Here is a blog post describing some of the projects I worked on during my tenure with SHA.
My main research interests are dimension reduction methods for high-dimensional data. This includes linear approaches (e.g. PCA, CCA, PCEV, PLS) as well as nonlinear approaches (e.g. manifold learning, autoencoders). High-dimensional data is challenging to analyse because of the so-called “curse of dimensionality”. However, we can mitigate this curse by using the structure in the data to our advantage.
I am interested in developing statistical methodologies that are statistically and computationally efficient. I am also interested in applications to statistical genetics, genomics, and neuroimaging.
You can find a list of projects I have worked on here.
- Sam Morrissette, PhD student in Statistics
- Asif Neloy, MSc student in Computer Science
- Madison Cranstone, undergraduate student
- James Young, undergraduate student
- Yuqing Wang, undergraduate student
- Thomas Czubryt, undergraduate student
- Jiyoung Kim, undergraduate student
- Wanmeng Wang, undergraduate student
- Joshua Hamilton, undergraduate student
Bhatnagar, S.R., Turgeon, M., Islam, J., Hanley, J.A., and Saarela, O. “casebase: An Alternative Framework For Survival Analysis and Comparison of Event Rates”. The R Journal, 14: 2022. doi:10.32614/RJ-2022-052.
Neloy, A. and Turgeon, M. “Feature Extraction and Prediction of Combined Text and Survey Data using Two-Staged Modeling”, 2022 International Conference on Data Mining Workshops (ICDMW), Orlando, USA, 2022.
Turgeon, M., Greenwood, C.M.T., and Labbe, A. “A Tracy-Widom Empirical Estimator For Valid P-values With High-Dimensional Datasets”. Submitted. arxiv
Farkas, C., Mella, A., Turgeon, M., and Haigh, J.J. “A novel SARS-CoV-2 viral sequence bioinformatic pipeline has found genetic evidence that the viral 3’ untranslated region (UTR) is evolving and generating increased viral diversity”. Frontiers in Microbiology, 12: 2021. doi:10.3389/fmicb.2021.665041.
Turgeon, M., Oualkacha, K., Ciampi, A., Miftah, H., Dehghan, G., Zanke, B.W., Benedet, A.L., Rosa-Neto, P., Greenwood, C.M.T., Labbe, A., for the Alzheimer’s Disease Neuroimaging Initiative. “Principal component of explained variance: an efficient and optimal data dimension reduction framework for association studies”. Statistical Methods in Medical Research, 27: 2018. doi:10.1177/0962280216660128.
Wang, Y., Murphy, O., Turgeon, M., Wang, Z.Y., Bhatnagar, S.R., Schulz, J., and Moodie, E.E.M. “The perils of quasi-likelihood information criteria”, Stat, 4: 2015. doi:10.1002/sta4.95
Ahmad, O.S., Morris, J.A., Mujammami, M., Forgetta, V., Leong, A., Li, R., Turgeon, M., Greenwood, C.M.T., Thanassoulis, G., Meigs, J.B., Sladek, R., and Richards, J.B. “A Mendelian randomization study of the effect of type-2 diabetes on coronary heart disease” Nature Communications, 6: 2015. doi:10.1038/ncomms8060
You can connect with me using any of the platforms below.