Big Data

The Big Data Group examines algorithmic and statistical aspects of large-scale data analysis.  Many of the most interesting and challenging problems in machine learning and data analysis lie at the intersection of statistics and computer science on the one hand and at the intersection of theory, implementations, and applications on the other hand.  By its nature, the research that we perform is highly interdisciplinary. 

Our research has examined:

  • Social network modeling
  • Random sampling and random projection methods
  • Communication-avoiding algorithms
  • Locally-biased graph algorithms
  • Numerically-intensive machine learning
  • Randomized linear algebra
  • Convex and non-convex optimization
  • Second-order optimization
  • Terabyte-scale implementations
  • Graph algorithm theory
  • Geometric network analysis
  • Parallel and distributed computing

In addition to the above, we are interested in a range of applications, and recent work has focused on problems in Astronomy, Climate science, Mass Spec imaging, Genetics, Multimedia Analysis, and Social Network Analysis.

The Big Data group is led by Michael Mahoney

Group Members