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UW “Trend in Engineering” Features Data Science
from UW CSE News: SeaFlow, a research instrument developed in the lab of UW School of Oceanography director Ginger Armbrust, analyzes 15,000 marine microorganisms per second, generating up to 15 gigabytes of data every single day of a typical multi-week-long oceanographic research cruise. UW professor of astronomy Andy Connolly is preparing for the unveiling of…
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UW CSE’s Jeff Heer is one of 14 Moore Foundation “Data-Driven Discovery Investigators”
from UW CSE News: The Gordon and Betty Moore Foundation joined last year with the Alfred P. Sloan Foundation in a process that ultimately selected the University of Washington, UC Berkeley, and New York University as partners in a 5-year, $38.7 million collaborative effort to advance data-intensive discovery. The Moore Foundation has just announced the…
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TED Talk: What’s the Next Window into Our Universe?
Big Data is everywhere — even the skies. In an informative talk, astronomer Andrew Connolly shows how large amounts of data are being collected about our universe, recording it in its ever-changing moods. Just how do scientists capture so many images at scale? It starts with a giant telescope … What’s The Next Window Into…
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ASPASIA: Adult Service Providers and Some Incidental Addenda
Project Lead: Sam Henly, a PhD student in the UW Department of Economics eScience Liaison: Andrew Whitaker, Data Scientist, eScience Institute Most prostitution in the United States is organized through Internet media. This presents an opportunity for research into a market that, historically, has proved impenetrable to systematic investigation. APSASIA is an effort to collect all of…
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Scalable Manifold Learning for Large Astronomical Survey Data
Project lead: Marina Meila, UW Department of Statistics eScience Liaison: Jake VanderPlas, Director of Research – Physical Sciences, UW eScience Institute Manifold Learning (ML), also known as Non-linear dimension reduction, finds a non-linear representation of high-dimensional data with a small number of parameters. ML is data intensive; it has been shown statistically that the estimation accuracy depends…
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Efficient Computation on Large Spatiotemporal Network Data
Project Lead: Ian Kelley, Ph.D., Research Consultant, Information School eScience Liaison: Andrew Whitaker, Ph.D., Research Scientist, eScience Institute The pervasive and rich data available in today’s networked computing environment provides many major opportunities for innovative data-intensive applications. Particularly challenging are data analysis projects that rely upon input from millions of sparse, highly dimensional, and dirty data files…
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Analysis of Kenya’s Routine Health Information System Data
Project lead: Gregoire Lurton, UW Institute for Health Metrics and Evaluation Advisors: Abie Flaxman and Emmanuela Gakidou, UW Institute for Health Metrics eScience Liaison: Daniel Halperin, Director of Research – Scalable Analytics, UW eScience Institute Every year, millions of dollars are spend on collecting data on health services in developing countries. This data then typically sits unused because…
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Using Social Media Data to Identify Geographic Clustering of Anti-Vaccination Sentiments
Project lead: Benjamin Brooks, UW Institute for Health Metrics and Evaluation Advisor: Abie Flaxman, UW Institute for Health Metrics and Evaluation eScience Liaison: Andrew Whitaker, UW eScience Institute There has been considerable attention given to the potential for search engine and social media data to provide real time information regarding public health threats; this idea is well known…
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Repeating Earthquake Detection Final Report
Project Lead: Alicia Hotovec-Ellis, Graduate Researcher, Earth and Space Sciences Advisor: John Vidale, Professor, Earth and Space Sciences eScience Liaison: Jake Vanderplas, Director of Research – Physical Sciences, UW eScience Institute In this project, we aimed to provide an open-source tool for seismologists to cluster repeating earthquakes in continuous data. The primary focus was to do this in…