Last spring Google University Relations announced an open call for proposals for Google App Engine Research Awards. We invited academic researchers to use Google App Engine for research experiments and analysis, encouraging them to take advantage of the platform’s ability to manage heavy data loads and run large-scale applications. Submissions included proposals in various subject areas such as mathematics, computer vision, bioinformatics, climate and computer science. We selected seven projects and have awarded each $60,000 in Google App Engine credits recognizing their innovation and vision.
Today we would like to share a brief introduction of the winning projects and their Principal Investigators:
- K. Mani Chandy, Simon Ramo Professor and Professor of Computer Science, California Institute of Technology
Cloud-based Event Detection for Sense and Response: Develop a low-cost alternative to traditional seismic networks. The image below is taken from the Community Seismic Network map showing active clients and events in real time.
- Lawrence Chung, Associate Professor, The University of Texas at Dallas
Google App Engine: Software Benchmark and Google App Engine Simulation Forecaster: Develop a tool to estimate software performance and cost on Google App Engine.
- Julian Gough, Professor, University of Bristol, UK
Personalised DNA Analysis: Develop a service that provides personal DNA analysis.
- Ramesh Raskar, PhD, MIT Media Lab; Dr. Erick Baptista Passos, IFPI (Federal Institute of Technology, Brazil)
VisionBlocks: develop a tool that delivers computer vision to people everywhere. The image below shows a current prototype implementation of VisionBlocks.
- Norman Sadeh, Professor, Director of Mobile Commerce Lab, School of
Computer Science, Carnegie Mellon University
Mapping the Dynamics of a City & Nudging Twitter Users: uncovering local collective knowledge about the a city using social media.
- William Stein, Professor of Mathematics, University of Washington
Sage: Creating a Viable Free Open Source Alternative to Magma, Maple, Matlab, and Mathematica.
- Enrique Vivoni, Associate Professor, Hydrologic Science, Engineering & Sustainability, Arizona State University
Cloud Computing-Based Visualization and Access of Global Climate Data Sets: provide scientific data on global climate trends.
|A dense network of seismic stations enables the Community Seismic Network to perform a finer-grained analysis of seismic events than possible with existing seismic networks.|
|Many algorithms are already included, and you'll be able create your own blocks as well.|
-Posted by Andrea Held, Program Manager, Google University Relations