Abstracts
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ZOE MOODIE
From the Canadian Prairies to Southern Africa: A Biostatistician's Journey in HIV Research
What does it take to build a scientific career across continents, disciplines, and decades 芒鈧�� and to do so as a woman? This talk traces my journey from undergraduate studies at a small Canadian university to leading the design and analysis of HIV vaccine clinical trials in Southern Africa, exploring the methodological challenges, human connections, and unexpected turns that shaped my work. I will reflect on what collaboration across disciplines, borders and cultures requires and how emerging AI tools are reshaping the landscape for the next generation of women in science.
DENIS TALBOT听:听
Untangling Bias: A Practical Beginner鈥檚 Guide to Causal Inference in Health Sciences
Association is not causation. Yet many health research questions require causal interpretation rather than simple associations. This interactive workshop introduces practical tools to assess whether an observed association can be interpreted causally and to identify key sources of bias (confounding, selection, and measurement bias). We will present two widely used frameworks for causal inference: the counterfactual framework and causal graphs, and discuss the conditions under which associations can be interpreted causally within each framework. We will also introduce structured tools to evaluate the risk of bias in health studies. The workshop is designed to be accessible to all; no formal prerequisites are required.
JANIE COULOMBE听:听
Where AI Stops and Research Begins: Creating Knowledge Beyond the Published Literature.
Artificial intelligence and conversational agents are becoming increasingly capable, raising an important question for biostatisticians: will our work still be relevant in ten years? When asked this question, ChatGPT provides a reassuring answer: Yes, but the role of biostatisticians will change. The tasks most vulnerable to AI are not the ones that define the best biostatisticians.
In this keynote, I will reflect on recent research projects and use them to explore how new methodological ideas emerge. Through these examples, I will discuss the unique strengths of biostatisticians: identifying important gaps in the literature, challenging existing assumptions, and transforming complex real-world problems into new research opportunities.
Rather than focusing on what AI can already do, we will consider what remains uniquely human: the ability to formulate meaningful questions, develop novel methodological frameworks, and create knowledge that does not yet exist in the scientific literature.
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