.NET Rocks!

Michael Isard on Dryad

Episode #378 Thursday, September 18, 2008

Richard and Carl talk to Michael Isard about Dryad, a grid computing and clustering project from Microsoft.


Michael Isard

Michael Isard received his D.Phil in computer vision from the Oxford University Engineering Science Department in 1998. In 1999 he started work as a Researcher at the Compaq Systems Research Center in Palo Alto, and he has worked for Microsoft Research in Silicon Valley since 2002. The majority of his early research was in the field of visual tracking and sequential filtering, and he helped to introduce particle filters to the computer vision community with the Condensation algorithm. From the time he joined Compaq his interests have broadened to include distributed systems research, and this is where he's spending the majority of his time at Microsoft.


Michael spent much of mid-2003 to early 2005 working closely with the MSN Search product group on the design and implementation of their V1 search engine. He was involved from the start of the project and owned several production components in the live query pipeline. He was particularly involved in the design and implementation of the monitoring and failure recovery systems, the query distribution and result aggregation system, and the inverted file lookup.


Current research projects cover a range from inference methods for Bayesian networks with applications in visual tracking and dense stereo estimation; to programming models for large-scale distributed systems and many-core processor architectures. Michael led the Dryad project to build a large-scale distributed execution platform for datacenters that is now widely used internally by Microsoft product groups. He has recently started working with several colleagues on AME, a new concurrent programming model that makes use of transactional memory without explicit atomic blocks. He has also been collaborating with Andrew Zisserman's Visual Geometry Group in Oxford on various aspects of object retrieval from large databases.

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