The UW eScience Institute invites short proposals (1-2 pages) for a remote one-quarter data-intensive research collaboration focusing on extracting insight from large, noisy, and/or heterogeneous datasets. The goal of the Data Science Incubator is to enable new science by bringing together data scientists and domain scientists to work on focused, intensive, collaborative projects. Their team of data scientists provides expertise in state-of-the-art technology and methods in statistics and machine learning, data manipulation and analytics at all scales, cloud and cluster computing, software design and engineering, visualization, and other topics. An information session will be held on Nov. 6th in-person and on Zoom. Learn more here.

The program is open to any faculty, postdoc, staff, or student whose research can be significantly advanced by intensive collaboration with a data science expert. To apply, we require a short project proposal describing the science goals, the relevant datasets, and the expected technical challenges.  The ideal proposal will clearly identify both the datasets involved and the questions to be answered, and will explain how the technical component of the project is critical to delivering exciting new findings. This year we are happy to announce the availability of cloud resources to support the incubator projects and we welcome applications that wish to use cloud computing.

On October 4, 2023 (12:30pm), CSDE Affiliate Adrian Raftery (Sociology & Statistics) will be giving a talk titled “Very Long-Term Probablistic Population Projections for Assessing the Social Cost of Carbon” to the UW’s Center for Statistics and the Social Sciences.  Raftery will be addressing how population forecasts are used by governments and the private sector for planning, with horizons up to about three generations (around 2100) for different purposes. The traditional methods are deterministic using scenarios, but probabilistic forecasts are desired to get an idea of accuracy, to assess changes, and to make decisions involving risks. In a major breakthrough, since 2015 the United Nations has issued probabilistic population forecasts for all countries using a Bayesian methodology. Assessment of the social cost of carbon relies on long-term forecasts of carbon emissions, which in turn rely on even longer-range population and economic forecasts, to 2300. We extend the UN method to very-long range population forecasts, by combining the statistical approach with expert review and elicitation. We find that, while world population is projected to grow for most of the rest of this century, it is likely to stabilize in the 22nd century, and to decline in the 23rd century.

Scott W. Allard, CSDE Affiliate and Evans School Professor of Social Policy will be presenting “Washington State Ballots Project: Understanding Rejections and Cures,” at the Evans Research Seminar.