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.
Faculty

Jeffrey Calder
Associate Professor
[email protected]
partial differential equations, numerical analysis, applied
probability, machine learning, image processing and computer vision

Gregory Handy
Assistant Professor
[email protected]
theoretical neuroscience, applied mathematics, stochastic processes, mathematical biology, dynamical systems, and calcium dynamics

William Leeb
Associate Professor
[email protected]
applied mathematics, computational harmonic analysis, signal and image processing, data analysis

Gilad Lerman
Professor
[email protected]
computational harmonic analysis, analysis of large data sets and statistical learning, bio-informatics

Yulong Lu
Assistant Professor
[email protected]
Mathematical foundations of machine learning and data sciences, applied probability and stochastic dynamics, applied analysis and PDEs, Bayesian and computational statistics, inverse problems and uncertainty quantification

Mitchell Luskin
Professor
[email protected]
numerical analysis, scientific computing, applied mathematics, computational physics

Andrew Odlyzko
Professor
[email protected]
computational complexity, cryptography, number theory, combinatorics, coding theory, analysis, probability theory, ecommerce, and economics of data networks
