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
jwcalder@umn.edu
partial differential equations, numerical analysis, applied
probability, machine learning, image processing and computer vision
Gregory Handy
Assistant Professor
ghandy@umn.edu
theoretical neuroscience, applied mathematics, stochastic processes, mathematical biology, dynamical systems, and calcium dynamics
William Leeb
Assistant Professor
wleeb@umn.edu
applied mathematics, computational harmonic analysis, signal and image processing, data analysis
Gilad Lerman
Professor
lerman@umn.edu
computational harmonic analysis, analysis of large data sets and statistical learning, bio-informatics
Yulong Lu
Assistant Professor
yulonglu@umn.edu
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
luskin@umn.edu
numerical analysis, scientific computing, applied mathematics, computational physics
Andrew Odlyzko
Professor
odlyzko@umn.edu
computational complexity, cryptography, number theory, combinatorics, coding theory, analysis, probability theory, ecommerce, and economics of data networks