Post-Graduate Stories
Evan Sidrow Will Explore “Fine-scale And Functional Mapping Of Brain Imaging Genetics, And Aging”
As a 2024 CANSSI Distinguished Postdoctoral Fellow, Evan Sidrow will take part in a comprehensive program that involves teaching, interdisciplinary or applied collaboration, professional development, and a research program that involves four projects in high-dimensional statistics and data science with application to health research and biostatistics. The projects will focus on brain imaging genetics, statistical genetics and host genetics of COVID-19, sweet spots as biomarkers for healthy aging, and epidemiology for pandemics. He will work under the supervision of Professors Lloyd Elliott (Simon Fraser University) and Junling Ma (University of Victoria).
Program: CANSSI Distinguished Postdoctoral Fellowship
Region: National
Date: 2024–2026
Project Focus Areas
The analysis of large scale consortia (such as UK Biobank and the Canadian Longitudinal Study on Aging (CLSA)) has led to remarkable conclusions about aging, neurodegenerative disease, and the genetic underpinning of brain development. As these consortia increase in scale, the statistical power to detect associations is also increasing. This allows researchers to investigate phenotypes at a finer scale: instead of operating on low-dimensional representations, they are gaining power to operate on the voxels within a brain area or to operate with time slices of a functional MRI signal. This increase in scale of biostatistical data requires that new statistical methodology be developed and explored for phenotypes such as brain images and metabolites (deep learning and functional data analysis). The project to which Evan Sidrow will contribute will provide novel statistical support and development of next-generation statistical software for a variety of large consortia examined in collaboration with the Department of Statistics and Actuarial Science at Simon Fraser University.
Evan will be involved in several collaborations that have approved access to data such as the CLSA and data from the Vancouver Island Health Authority (Island Health) that will provide training and experience with application of high-dimensional statistics to big data, including brain imaging, genetics, metabolomics, and patient histories and patient outcomes.
He will also have opportunities to co-supervise graduate students, organize workshops (including a planned workshop with CANMOD: the Canadian Network for Modelling Infectious Diseases), teach one course during each year of the program, and present at international conferences. He may also coach case study competitions or supervise undergraduate summer research assistants.
Getting to Know Evan
Evan Sidrow completed a master’s degree at the University of Colorado and spent two years as a data scientist at Seagate Technology before beginning his PhD studies at the University of British Columbia under the supervision of Nancy Heckman and Marie Auger-Méthé. His research focuses on stochastic processes for ecological biologging data.
He sees his CANSSI Distinguished Postdoctoral Fellowship as a chance to explore his professional interests while becoming a more well-rounded researcher and educator and notes that his CDPF project gives him the opportunity to solve deep questions about complex datasets with strong applied motivations. He also sees connections between the interdisciplinary nature of the CDPF project and the interdisciplinary component of his PhD research and is looking forward to working with multiple advisors and collaborators from different universities across Canada to grow his research network.
In addition to the research opportunities, Evan hopes to develop his teaching skills through the CDPF program.
I have considered a career in teaching and would like to further explore this option through the CANSSI [Distinguished Postdoctoral fellowship].
About the Supervisors
Junling Ma
Junling Ma is an associate professor in the Department of Mathematics and Statistics at the University of Victoria.
He received a BSc in Applied Mathematics in 1994 and an MSc in Applied Mathematics in 1997 from Xi’an Jiaotong University in China. He received a PhD in Applied Mathematics from Princeton University in 2003.
His research interests include mathematical modelling in epidemiology, ecology, and evolutionary biology using the tools of differential equations and dynamical systems, stochastic processes, and statistical methods.
Current projects include modelling the seasonal epidemics and pandemics of influenza; the spread of HIV among injection drug users and sex workers; plague in the 14th to 16th centuries in London; network epidemic models; and statistical methods on disease parameter estimation.
Lloyd Elliott
Dr. Lloyd T. Elliott is an assistant professor in the Department of Statistics and Actuarial Science at Simon Fraser University, and an honorary academic visitor at the Nuffield Department of Clinical Neuroscience, Oxford. He received his PhD in Machine Learning from the Gatsby Computational Neuroscience Unit at University College London, where he did his thesis on Bayesian nonparametric models for genetic variation.
After his PhD, Lloyd did postdoctoral research in statistical genetics at the University of Oxford. His research includes brain imaging genetics in UK Biobank, and high-dimensional statistical models. Since the COVID-19 pandemic emerged, Lloyd has provided statistical support to epidemiological work funded by Genome BC and has served on the Genetic Epidemiology Sub-Committee of HostSeq.
His areas of interest include statistical genetics and genomics, bioinformatics, machine learning, Bayesian statistics, high-dimensional data.
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