livinglabs.rutgers.edu
Resources
http://livinglabs.rutgers.edu/resources.html
Rutgers School of Environmental and Biological Sciences. New Jersey Agricultural Experiment Station. The Office of Agriculture and Urban Programs. What Are Living Labs? Living Labs and Sustainability. Implementation: A Collaborative Effort. Office of Agriculture and Urban Programs. Follow us on Facebook. Follow us on Google. Follow us on LinkedIn. Follow us on Twitter. Follow us on YouTube. Living Laboratories for the Cook/Douglass Campus ( PDF. Social Space and Sustainability on Cook/Douglass ( issuu.
github.com
RogerTangos (Albert Carter) · GitHub
https://github.com/RogerTangos
X61;lbert.r.carter@gmail.com. Http:/ livinglab.mit.edu. Apr 3, 2012. Graph based JSON-driven text reader. A Stud.IP plugin that adds telescopic-text-like markup. An example application demonstrating a connection to datahub.csail.mit.edu. A simple light alarm for waking up. Github pages repo for the p.irateship.com website. 1,143 contributions in the last year. Summary of pull requests, issues opened, and commits. Learn how we count contributions. Pushed 4 commits to CSAIL-LivingLab/bigdawg 2a.
bigdata.csail.mit.edu
DATA PARTNERS | bigdata@CSAIL
http://bigdata.csail.mit.edu/node/105
Skip to main content. We partner with different organizations to make data sets available for research here at MIT - for exploring new ideas; for testing out theories and new algortihms, systems and tools; for student projects and challenges; and ultimately, for demonstrating the impact of Big Data with real world data. Data Resources: MGH and The Laboratory for Quantiative Medicine. Additional Information on Data Resources and Applications. By James Michaelson, PhD. Seminar Series on Quantiative Medicine.
bigdata.csail.mit.edu
people | bigdata@CSAIL
http://bigdata.csail.mit.edu/people
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bigdata.csail.mit.edu
BIG DATA ADVISORS | bigdata@CSAIL
http://bigdata.csail.mit.edu/node/220
Skip to main content. We would like to acknowledge and sincerely thank the following Advisors for the time and input they have dedicated to the development and growth of the Big Data Initiative at CSAIL:. Heather Wilson, Chief Data Officer (Advisory Board Member). Ozerk Gogus, VP, Strategic Business Partner, Global Data Solutions. Tomasz Motyl, Chief Development Officer (Advisory Board Member). Chris Bilton, Director of Research and Technology. Deborah Stokes, Leader, Global External Research. Vianney Ko...
bigdata.csail.mit.edu
Partners | bigdata@CSAIL
http://bigdata.csail.mit.edu/data
Skip to main content. We partner with different organizations including industry, government, non-profits and other universities. Provide the Initiative with invaluable funding support. As a member, organizations have unique access to Big Data research at MIT. It is also an opportunity for corporations to share and discuss their real world challenges and concerns. And data challenges; and ultimately, for demonstrating the impact of Big Data with real world data.
bigdata.csail.mit.edu
Members | bigdata@CSAIL
http://bigdata.csail.mit.edu/members
Skip to main content. The MIT Big Data Initiative acknowledges our industry partners for their generous support. AIG, Alior Bank, British Telecommunications (BT), EMC, Facebook, Huawei, Intel, Microsoft, Quanta, Shell, Thomson Reuters.
bigdata.csail.mit.edu
Projects | bigdata@CSAIL
http://bigdata.csail.mit.edu/projects
Skip to main content. The MIT Big Data Initiative supports a number of special projects and activities, including:. Big Data and Privacy Workshop (JUNE 2013) and Working Group. MIT Big Data Challenge.
bigdata.csail.mit.edu
Approach | bigdata@CSAIL
http://bigdata.csail.mit.edu/approach
Skip to main content. We believe the solution to big data is fundamentally multi-disciplinary. Our approach brings together world leaders in parallel architecture, massive-scale data processing, algorithms, machine learning, visualization, and interfaces to collectively identify and address the fundamental technology challenges we face with Big Data. Our approach focuses on four broad research themes, summarized below:. Machine Learning and Understanding. Machine Learning and Understanding.
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