Date/Time

Date(s) - 04/21/2021
4:30 pm - 5:30 pm

Please use this zoom link for the event.

Please join us for a UW Data Science Seminar event on Wednesday, April 21st from 4:30 to 5:30 p.m. The seminar is the last of three seminars that feature projects from this year’s Winter Incubator program.

2021 Winter Incubator projects

The goal of our Winter Incubator program is to enable new science by bringing together data scientists and domain scientists to work on focused, intensive, collaborative projects. Our 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. Read more about the 2021 projects here.

“The Universe of International Treaties” – Bree Bang-Jensen, Department of Political Science

Abstract: The greatest challenges of the 21st century are cross-national, including climate change, migration, epidemics, inequality and financial corruption.  As a result, it is critical that we better understand the factors that endanger international cooperation. Despite a wealth of research on how the design of international treaties affects treaty commitment and compliance, we only have snapshots of how delegation to third parties, enforcement, and precision differ across treaties.  Because most treaties are publicly available text documents, this research area provides a veritable goldmine for the application of cutting edge NLP/machine learning tools—trained on highly curated datasets—to the messy, real world data of most interest and value to addressing pressing social science questions.

We will work to identify the frequency of these different elements of treaty design and legalization with the help of a stratified sample of 2,000 human labeled treaties. We might use these human labels to create a supervised machine learning model that then can predict labels for the universe of 55,000 treaties.  Alternately, we may use natural language preprocessing strategies to handle the idiosyncrasies of these data. This project will help future treaty negotiators better understand the features of legalization that improve treaty durability and compliance and thus draft treaties that better contribute to cooperative outcomes, and second, detailed data on treaty design will enable other researchers in political science, sociology, economics and international law to research questions and test hypotheses that are currently not possible to explore due to limited data.

“Using Social Media to Model Backcountry Use in Rainier National Park” – Gabriel Wisswaesser, School of Environmental & Forest Sciences

Abstract: As public land use increases, accurate visitation numbers are paramount to managers and researchers interested in mitigating and understanding anthropogenic effects. Alpine water quality, as of late, has been under exceptionally high pressure because human waste mitigation is not keeping up with increases in backcountry use. Data concerning backcountry visitation is sparse, and attempts to model it accurately in National Parks is an essential next step in quantifying use. Using publicly available, geotagged, social-media data, and other variables to predict visitation this project began the initial steps in modeling remote locations in Mount Rainier National Park (MORA). Collaborating with MORA officials, this project delineated park regions that had useful existing trail-count data, prioritizing those locations that are isolated and remote with only one access route. Social media posts that fell within these designated spatial zones were combined with the other predictive factors like precipitation, institutional closures, and week-of-year to estimate visitation, based on relationships of variables with on-site counts of hikers. The foundational model was one parameterized, through the work of Spencer Wood, in Mount Baker-Snoqualmie National Forest (MBS). Preliminary results show when on-site MORA counts are regressed against MBS model predictions there is an R 2 of 0.39 and a Pearson’s of 0.62. These initial results show promise and with the addition of a categorical variable for location and random effects, predictive ability will hopefully increase.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.

All seminars will be hosted virtually for the 2020-2021 academic year, and are free and open to the public.