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A Machine Learning for Mobile Health workshop, part of the upcoming Neural Information Processing Systems Conference (NeurIPS 2020), is inviting contributions and extended abstracts from researchers and clinicians in the interdisciplinary machine learning and mobile health space, with the goal to better address the various challenges currently facing the widespread use of mobile health technologies in health and healthcare. Co-organized by Duke Statistical Science assistant professor Katherine Heller, PhD who is also a research scientist at Google AI, the workshop aims to facilitate collaboration between machine learning researchers, statisticians, mobile sensing researchers, human-computer interaction researchers, and clinicians from around the world.

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Seven Duke +DS learning experiences will be held in September. These sessions offer the opportunity to dive deeper into topics and target diverse units at Duke: from those that desire a broad understanding of what is possible with data science, and those who wish to use data-science tools (software) without a need for deep understanding of underlying methodology, to those who desire a rigorous technical proficiency of the details and methodology of data science. Anyone in the Duke community is welcome to join, there is no fee to attend, and no prior experience is necessary.

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Across the world and in the United States, multiple studies have shown that social distancing is effective at reducing the spread of SARS-CoV-2 both at interpersonal and statewide levels. An early analysis of social distancing in the United States amid the COVID-19 pandemic, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge by Duke undergraduates Shannon Houser and Jack Lichtenstein, echoed those findings and won the “Best Visualizations” prize at the contest. Using data available from Google Mobility Reports, the duo explored how factors such as population density, initial number of positive coronavirus cases per capita, governor’s political affiliation, and official shelter-in-place orders influenced the magnitude of a state’s social distancing early during the COVID-19 pandemic.

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Aside from altering the very fabric of daily life across the United States and the world, the COVID-19 pandemic has exposed the many existing shortcomings and inequities of the American healthcare system. The burgeoning public health crisis has resulted in more than 5 million confirmed cases nationwide and close to 163,000 deaths as of the beginning of August. However, some communities and groups have been disproportionately impacted, as a prize-winning analysis by Duke’s Meredith Brown, Matt Feder, and Pouya Mohammadi, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge.

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The Duke University School of Medicine’s Office of Data Science and Information Technology and Duke AI Health partnered to co-host a virtual symposium on Wednesday, June 24, focused on using data science to combat public health crises. In her opening remarks, School of Medicine Dean Mary E. Klotman said that we are in the midst of two pandemics—COVID-19 and racism. The School of Medicine, of which the DCRI is part, will play an active role in driving solutions to both of these pandemics, and data science is one of the key tools that will be used. The symposium, titled “Public Health Crises of 2020: Battling COVID-19 and Disparities with Data,” featured seven DCRI faculty and data science experts from a range of other Duke entities such as Duke Forge and AI Health. The event also featured speakers external to the University, including two keynote speakers from the NC Department of Health and Human Services, as well as speakers from Change Healthcare and Amazon Web Services Data Exchange. The event, which was delivered in rapid-fire five-minute talks, was hosted and moderated by the DCRI’s Michael Pencina, PhD, Vice Dean for Data Science & Information Technology (pictured left). Other DCRI speakers included Jessilyn Dunn, PhD; Benjamin Goldstein, PhD; Ricardo Henao, PhD; Keith Marsolo, PhD; Susanna Naggie, MD; and DCRI fellow Jedrek Wosik, MD.

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