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Meet the DSSG 2023 Teams
The eScience Institute’s annual Data Science for Social Good (DSSG) program kicks off next week, and we are excited to host a new group of Student Fellows at the WRF Data Science Studio throughout the summer. 2023 marks the ninth year of DSSG, and two teams will spend the next ten weeks collaborating with our…
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Generating regionally integrative datasets to understand groundwater insecurities in the Colorado River Basin
Project leads: Akshay Mehra, Assistant Professor of UW Earth and Space Sciences, and Sameer Shah, Assistant Professor of UW Environmental and Forest Sciences Data scientist: Vaughn Iverson DSSG fellows: Yuanning Huang, Kimberly Kreiss, Maia Powell, and Aanchal Setia. Get the participant bios here. The Colorado River Basin (CRB) is experiencing unprecedented water scarcity. Arguably, the CRB is an…
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Heating Pumps in Alaska and Beyond
Project leads: Erin Trochim, Research Assistant Professor, Alaska Center for Energy and Power, University of Alaska Fairbanks Data scientist: Maddie Gaumer DSSG fellows: Aminat Adefolu, Silas Gifford, Katherine Grisanzio, and Brian Leung. Get the participant bios here. Decarbonization, a pressing global issue, necessitates the transition from carbon-intensive power to net-zero sources. The Arctic is an area of particular concern,…
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Investigating Structure of Social Science Research Datasets for Better ML Evaluation
Project Lead: Bernease Herman, eScience Data Scientist Specialized machine learning architectures, such as deep learning, typically rely on inductive biases and other data-specific correlational structure information to produce more effective models. Similarly, the design and evaluation of differentially private synthesizers depends heavily on the correlational structure of the datasets most commonly used in the field. We…
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Wetland Communities in the US
Project Lead: Celina Balderas Guzman, UW Assistant Professor of Landscape Architecture Data Science Lead: Spencer Wood Sea level rise threatens human communities on the coast with a variety of hazards including erosion and flooding. However, these risks are reduced in locations where coastal wetlands provide a natural buffer that absorbs the force of waves created by storms.…
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Characterizing the spatio-temporal evolution of marine heatwaves
Project Lead: Cassia Cai, UW Oceanography Faculty Advisor: LuAnn Thompson, UW Oceanography Data Science Lead: Valentina Staneva Marine heatwaves (MHWs) are defined as discrete periods when local sea surface temperatures (SST) exceed a temperature threshold (e.g., a seasonally varying temperature threshold). A number of high-profile MHWs, such as the Great Barrier Reef 2002, Mediterranean Sea 2003 and 2006,…
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The Prototype of a Cloud Store for Distributed Acoustic Sensing Data
Project Lead: Yiyu Ni, Earth and Space Sciences, UW College of the Environment Data Science Leads: Naomi Alterman and Rob Fatland Distributed Acoustic Sensing (DAS) is an emerging seismic observation method that has recently enabled entirely new types of geophysical observation. DAS utilizes repeated laser pulses along optical fibers up to ~100 km in length to measure phase changes…
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Constructing a robust metric of peak quality for untargeted mass-spectrometry
Project Lead: Will Kumler, UW Oceanography Faculty Advisor: Anitra Ingalls, UW Professor of Chemical Oceanography Data Science Lead: Bryna Hazelton Mass spectrometry is a cutting-edge analysis field used to identify the molecular composition of samples taken from medical laboratories, the depths of the ocean, and even outer space. In the Ingalls Lab at UW, we use it to…
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Leveraging large satellite archives to understand the timing and distribution of global snowmelt
Project Lead: Eric Gagliano, Terrain Analysis and Cryosphere Observation Lab, UW Civil & Environmental Engineering Faculty Advisor: David Shean, UW Civil & Environmental Engineering Data Science Lead: Scott Henderson Seasonal snow plays an essential role in the Earth system and more than 1/6 of the world’s population relies on runoff from seasonal snow and glaciers for agricultural and…
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Announcing the 2023 Data Science Incubator Projects
The eScience’s annual Data Science Incubator program kicked off last week, which enables new research discoveries by bringing together data scientists and domain scientists to work on focused, intensive, collaborative projects. Our team of data scientists provide expertise in state-of-the-art technology and methods in large-scale data manipulation and analytics, cloud and cluster computing, statistics and…