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Professor Michael Mahoney joined ICSI earlier this year as a principal investigator working on big data research, a rapidly growing area of computer science. As the sheer amount of available data increases, computer scientists are looking for efficient methods of analyzing and using massive amounts of data. Big data research has the potential to influence many areas of not only computer science, but other scientific disciplines as well.

Michael, who was previously at Stanford, and now splits his time between ICSI and UCB, has several research directions. For example, he is currently working on an integrated treatment of statistical and computational issues in collaboration with researchers at UC Berkeley and University of Illinois. The research provides new insight into the existing algorithms, produces innovative methodologies for analyzing large-scale data, inspires new lines of quantitative investigations in interdisciplinary research, and offers a unique educational experience. He is also researching the development of scalable statistics and machine learning algorithms that can operate on real-world datasets produced by a diverse range of experimental and observational facilities with Lawrence Berkeley Laboratory and UC Berkeley scientists. The development of such algorithms for real-world data is a critical component of big data research.

In conjunction with scientists from UC Berkeley, Stanford, and Rensselaer Polytechnic Institute, Michael is also organizing the 2014 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2014), which will be held June 17-20 on the UCB campus. Registration information is available here: http://mmds-data.org/home/registration2014. The workshop will address algorithmic, mathematical, and statistical challenges in modern statistical data analysis. The goals of MMDS 2014 are to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and internet data sets, and to bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to promote cross-fertilization of ideas.