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.

Seminars

There are two weekly seminars:

Faculty

Jeffrey Calder

Jeffrey Calder 

Associate Professor 

jwcalder@umn.edu
partial differential equations, numerical analysis, applied
probability, machine learning, image processing and computer vision

William Leeb

William Leeb

Assistant Professor

wleeb@umn.edu
applied mathematics, computational harmonic analysis, signal and image processing, data analysis

Gilad Lerman

Gilad Lerman

Professor

lerman@math.umn.edu
computational harmonic analysis, analysis of large data sets and statistical learning, bio-informatics

mitch-luskin_0.png

Mitchell Luskin

Professor

luskin@math.umn.edu
numerical analysis, scientific computing, applied mathematics, computational physics

Andrew Odlyzko

Andrew Odlyzko

Professor

odlyzko@math.umn.edu
computational complexity, cryptography, number theory, combinatorics, coding theory, analysis, probability theory, ecommerce, and economics of data networks

li-wang.png

Li Wang

Assistant Professor

wang8818@umn.edu
numerical analysis, scientific computing, applied math

More faculty research areas