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William Denault’s work is at the intersection of statistics, AI, genetics, and epidemiology. Denault’s research leverages his experiences as an applied statistician in genetic epidemiology to develop interpretable models for analyzing complex molecular datasets. Denault mostly focuses on devising novel approaches to better understand disease etiology, characterizing genetic variants, inferring causal pathways, and elucidating early embryonic development mechanisms. Denault is particularly interested in leveraging information from recent reports on dynamic gene regulation in induced pluripotent stem cell differentiation to study the effect of early-life genetic regulation on later-life adverse outcomes without using an invasive approach. While Denault is a statistician by training, he is interested in empowering the statistical approaches he developed with machine learning. In particular, Denault aspires to develop methods that preserve statistical rigor without compromising the flexibility offered by machine learning approaches.


Willam Denault is a postdoctoral researcher in the Stephens l Lab at the University of Chicago. He received his Ph.D. in statistical genetics from the University of Bergen, Norway, and his master’s in statistics from the University of Strasbourg. His research interests lie in statistical genetics and population dynamics, focusing on devising methods to better understand molecular genetics regulation and the cell dynamics during early embryogenesis. In particular, he is working toward understanding the effect of early life regulation on later life outcomes.