UW Data Science Seminar: Winter Incubator


4:30 pm – 5:20 pm


Please join us for a UW Data Science Seminar on Wednesday, March 27th from 4:30 to 5:20 p.m. PST. The seminar will feature two projects from Diane Xue and George Brencher, who participated in our 2024 Data Science Incubator program at the eScience Institute.

2024 Spring Quarter seminars will be held in PAA A118 – campus map


Polygenic and Contextual Determinants of Alzheimer’s Disease and Related Dementias

Abstract: The goal of this project is to model multi-level macro- and meso- environmental factors including ambient pollutants, socioeconomic status, density of physical activity facilities and social engagement destinations. alongside polygenic scores that summarize individual-level genetic risk for AD in order to determine what social and environmental factors remain significantly associated with dementia risk and/or cognitive decline after controlling for PRS.  Additionally, we want to investigate whether effects of social and environmental factors differ for high- and low- genetic risk groups. Social, built, and physical environmental variables that are associated with healthy controls who are at high genetic risk can be further investigated as population-level solutions for promoting AD resilience. Furthermore, early prediction of AD is key to prevention. The results of the proposal will prepare us to integrate genetic and non-genetic factors for risk prediction, moving us close to precision treatments.

Characterizing glacial lake outburst flood hazard at a regional scale using fused InSAR-speckle tracking surface displacement time series

Abstract: Using satellite synthetic aperture radar remote sensing, we have developed a workflow allowing us to quantify surface changes that can contribute to glacial lake outburst flood (GLOF) likelihood, including landslide movement and moraine dam subsidence. Our approach fuses interferometric synthetic aperture radar (InSAR) and SAR speckle tracking data to accurately capture deformation as fast as hundreds of meters per year and as slow as <1 cm per year. During this incubator project, we developed infrastructure to deploy our workflow on the cloud using Github Actions, allowing us to quickly and efficiently process large radar datasets and create surface displacement time series. We applied this processing pipeline to measure surface displacement from 2017-present day with high spatial and temporal resolution for the areas surrounding selected hazardous glacial lakes in Nepal, India, and China. The resulting multi-year displacement time series allow us to detect and track intra- and inter-annual changes of dynamic landslide, permafrost, and glacial features and precisely quantify rates of moraine dam subsidence, significantly improving our understanding of GLOF hazard and providing a critical missing input to existing risk analysis frameworks. We use radar data acquired in two orientations to decompose surface displacement into vertical and horizontal components, allowing us to understand the contribution of ice melt, ice flow, and other processes to ground movement and to quantify how those processes change on seasonal and yearly time scales. These results have implications not only for GLOF hazard, but also alpine geomorphology and glaciology, as we learn about processes associated with thinning and retreat of debris-covered glaciers.

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.


The 2023-2024 seminars will be held in person, and are free and open to the public.