Data Science

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More about data science

Research in data science covers many areas in both mathematical theory and applications.

Research topics include

  • Robust estimation and recovery problems
  • Optimization
  • High-dimensional statistics
  • Manifold learning and modeling
  • Deep learning
  • Computer vision and imaging
  • Economics
  • Graph-based learning
  • Data assimilation
  • PDEs and machine learning

The data science group also has numerous interactions with other departments through the University of Minnesota's Data Science Initiative and with other universities.


There are two weekly seminars:


Jeffrey Calder

Jeffrey Calder 

Associate Professor
partial differential equations, numerical analysis, applied
probability, machine learning, image processing and computer vision

William Leeb

William Leeb

Assistant Professor
applied mathematics, computational harmonic analysis, signal and image processing, data analysis

Gilad Lerman

Gilad Lerman

computational harmonic analysis, analysis of large data sets and statistical learning, bio-informatics


Mitchell Luskin

numerical analysis, scientific computing, applied mathematics, computational physics

Andrew Odlyzko

Andrew Odlyzko

computational complexity, cryptography, number theory, combinatorics, coding theory, analysis, probability theory, ecommerce, and economics of data networks


Li Wang

Assistant Professor
numerical analysis, scientific computing, applied math