I hold a Ph.D. in Medical Science from the University of Toronto, where my research centered on using blood biomarkers to characterize alterations in the immune system in response to Traumatic Brain Injury (TBI). During my postdoctoral work at the University of Toronto, St. Michael’s Hospital, and the Centre for Addiction and Mental Health, I expanded my investigations to include the biological underpinnings of clinical conditions such as sport concussion, post-traumatic stress disorder, addiction, and other mental health disorders.

Throughout my academic journey, I grew increasingly frustrated with traditional statistical approaches which I found to be limited and rigid, often failing to provide actionable and interpretable conclusions from my data. This led me to discover Bayesian statistics, which made it more accessible for me to move beyond null hypothesis significance testing to flexible statistical modelling. The main reason I gravitated to this approach is because it became simpler to leverage domain expertise and focus on the underlying data-generating processes to derive meaningful insights from data.

Today, as a scientist and applied statistician with experience in academia, government and industry, I employ Bayesian methods to create tailored statistical models that deliver clear and actionable conclusions from datasets. My work aims to bridge the gap between rigorous scientific inquiry and practical, real-world applications.