This page describes various activities in data-intensive distributed computing applications (aka "cloud computing") that I'm involved in at the University of Maryland. One major focus is the application of MapReduce to solve problems in text processing (using the open-source implementation Hadoop).

Research

• I'm the lead faculty at Maryland in the Google/IBM Academic Cloud Computing Initiative.
• Philip Resnik and I are funded by the National Science Foundation to explore applications of MapReduce to machine translation and cross-lingual text mining.
• Check out my publications page for papers I've written on cloud computing, MapReduce, etc.
• Cloud⁹ is an open-source MapReduce library for Hadoop that I use for teaching and my own research.
• CloudBurst, a short read mapping algorithm (for DNA sequence analysis) implemented in MapReduce, started off as a course project in my cloud computing course in Spring 2008. See journal article in Bioinformatics.
• Crossbow is a Hadoop-based version of Bowtie, a short read aligner based on the Burrows-Wheeler Transform. It started off as a course project in my Spring 2009 cloud computing course.

Education

• My cloud computing course, version 2.0 (Fall, 2008)
• My cloud computing course, version 1.0 (Spring, 2008)

Outreach

• In June, 2009, I co-presented a tutorial with Chris Dyer titled "Data-Intensive Text Processing with MapReduce" at the 2009 North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL/HLT 2009).
• In April 2009, I spoke at the Washington DC Hadoop Users Group (HUG) Meeting. Here are the slides from my presentation.
• In March, 2009, I taught a short course on cloud computing and Hadoop.
• In May, 2008, I taught a half-day tutorial at the 2008 HCIL Symposium on cloud computing and Hadoop
• In March, 2008, I spoke at the Hadoop Summit sponsored by Yahoo!

Press

• I was quoted in an Washington Post article "Tech Firms Seek to Get Agencies on Board With Cloud Computing" (March 31, 2009)
• Google/IBM Academic Cloud Computing Initiative (October 8, 2007): [Google press release] [IBM press release] [UMD press release]

This work is or has been supported by the following sources: NSF under awards IIS-0836560 and IIS-0705832; IBM and Google under the Academic Cloud Computing Initiative (ACCI); the Intramural Research Program of the NIH, National Library of Medicine; DARPA/IPTO Contract No. HR0011-06-2-0001 under the GALE program; and Amazon Web Services. Any opinions, findings, conclusions, or recommendations expressed here do not necessarily reflect those of the sponsors.