The NSF’s CISE Directorate signed a memorandum of understanding with Microsoft in February 2010, facilitating free access to the Windows Azure cloud computing platform for select research teams. Today, CISE and Microsoft are announcing 13 cloud computing research projects funded for the next two years through this partnership. The objective is “to make simple yet powerful tools readily available to researchers to extract insights [throughout science] by mining and combining diverse data sets.”
From the official NSF press release:
Microsoft will provide the 13 cloud computing research projects identified by NSF through its rigorous peer review process with access to Windows Azure–a cloud computing platform that provides on-demand computing and storage to host, scale and manage Web applications on the Internet through Microsoft data centers–for a two-year period, along with a support team to help researchers quickly integrate cloud technology into their research. Microsoft researchers and developers will work with grant recipients to equip them with a set of common tools, applications and data collections that can be shared with the broad academic community.
“Cloud computing represents a new generation of technology in this new era of science, one of data-driven exploration. It creates precedent-setting opportunities for discovery,” said Farnam Jahanian, assistant director of the NSF Directorate for Computer and Information Science and Engineering. “We are especially proud of these excellent projects, led by top researchers at universities throughout the country that we think will best capitalize on the NSF/Microsoft partnership. They will use the resources Microsoft will provide to explore and experiment with cloud computing in order to address some of society’s greatest challenges.”
The 13 projects and PIs — selected through peer review — span a wide range of research topics such as computing, biology, and energy:
- Building scalable trust in cloud computing — Kenneth Birman, Cornell
- Bettering interactive protein-protein docking — Audrey Tovchigrechko, Venter Institute
- Enhancing Stork Data Scheduler for Azure — Tevfik Kosar, SUNY-Buffalo
- Utilizing continuous bulk processing — Kenneth Yocum, UCSD
- Enabling mobile cloud computing — Richard Han, CU Boulder
- Refining language models using web-scale language networks — Qiaozhu Mei, U of Michigan
- Predicting transcription factor binding sites for genes — Zhengchang Su, UNC-Charlotte
- Managing large watershed systems — Jonathan Goodall, U of South Carolina, and Marty Humphrey, UVa
- Tackling large scale graph problems — Viktor Prasanna, USC
- Storing data with minimal trust — Michael Walfish, UT-Austin
- Understanding relational data markets — Magdalena Balazinska, U of Washington
- Conducting intensive biocomputing — Wuchun Feng, Virginia Tech
- Effectively and widely using renewable energy sources — Kwa-Sur Tam, Virginia Tech
Read the full press release to learn more!
(Contributed by Erwin Gianchandani, CCC Director)
