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Darren Tanner
Senior Applied Research Scientist at Microsoft
Darren Tanner is a Senior Applied Research Scientist at Microsoft, where he has been working since February 2019. Based in Redmond, Washington, he focuses on developing data science, machine learning, and AI tools that aim to address significant global challenges. As a core member of Microsoft's AI for Good research lab, he collaborates with various non-profits and research institutions to leverage data for humanitarian efforts and environmental preservation.1
His professional background includes leading data science initiatives to combat corruption within Microsoft's partner ecosystem and building machine learning tools for digital safety. Prior to his role at Microsoft, Tanner was a Data Science Fellow at Insight Data Science, where he created an interactive app called cocktailVision that utilized computer vision and deep learning to suggest cocktail recipes based on images of liquor collections.1
Tanner holds a Ph.D. in Linguistics from the University of Washington, with a focus on cognitive neuroscience and language processing. His academic career includes positions as an Assistant Professor at the University of Illinois at Urbana-Champaign and as a Postdoctoral Researcher at Penn State University, where he managed research projects and trained students in experimental design and data analysis.1
Highlights
Earlier this week, in collaboration with researchers at Seattle Children's and The University of Auckland, as well as Juan M. Lavista Ferres and William Brinson Weeks, MD, PhD, MBA in our group, we published a report in JAMA Pediatrics where we were the first to find a systematic association between maternal pre-pregnancy obesity and subsequent risk for sudden unexpected infant death (SUID). This was a nationwide study using CDC data that included data from over 18 million US births over a 5 year period.
We found a clear dose-response relationship between maternal pre-pregnancy BMI and SUID risk: after controlling for confounders, higher BMI was associated with a continuously increasing risk of SUID.
Approximately 5.4% of SUID cases could be attributable to high maternal BMI, again after controlling for confounders.
We do not yet know the exact causal mechanism driving this association. But we hope that this work spurs further research work to understand the link and find new ways to prevent these deaths.
We also want to underscore that the added risk conferred by high maternal BMI at the individual is small in relation to some risk factors, such as smoking. However, we need to consider the total population-level risk, given that global obesity rates are high and rising.
See our most recent work on risk factors for stillbirth, where Juan M. Lavista Ferres, Edwin Mitchell and I use advanced statistical methods to improve our understanding of the relationship between birthweight and stillbirth risk.
