Category: Incubator Project

• Students’ Sleep and Academic Performance

  

  Project Lead: Ângela M. Katsuyama, UW Biology Advisor: Horacio O. de la Iglesia, UW Biology eScience Liaisons: Bill Howe, Daniel Halperin This project investigates the impact of sleep in college academic performance. We hypothesize that poor academic performance in college students correlates with poor sleep behaviors. To address this hypothesis, we collected data from 72 senior students…

• Kernel-Based Moving Object Detection

  

  Project Lead: Andrew Becker, UW Astronomy eScience Liaison: Daniel Halperin With assistance from: Andrew Whitaker, Bill Howe Kernel-Based Moving Object Detection (KBMOD) describes a new technique to discover faint moving objects in time-series imaging data. The essence of the technique is to filter each image with its own point-spread-function (PSF), and normalize by the image noise, yielding a likelihood…

• 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…

• 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…

• 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…

• 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…

• 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…

• 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…