Statistics and Data Science
Statistics and data science are fields of applied mathematics designed to interpret data in all its many forms. This is done by applying mathematical models and statistical theory that relate the data at hand to the underlying questions and often hidden features of interest. A key advantage of statistical science is the ability to quantify the uncertainty in a prediction or decision and for decision making this aspect is often as important as the estimate itself. The AMS department has projects that cover a wide range of different areas of application including environmental and climate science, renewable energy, air quality, and advanced manufacturing. Moreover these areas involve statistical methodology such as curve and surface fitting, Bayesian statistics, and statistical computing, that can be easily transferred to other areas of science and engineering.
Research Faculty
Soutir Bandyopadhyay
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Research Group: Spatial Statistics
- Spatial and Environmental Statistics
- Time Series and econometrics
- Bioinformatics
- Bootstrap/Resampling methods
- Large Sample Theory
GREG FASSHAUER
- Meshfree Approximation Methods
- Radial Basis Functions
- Approximation Theory
- Numerical Solution of PDEs
- Spline Theory
- Computer-Aided Geometric Design
Dorit Hammerling
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Research Group: Methane Monitoring
- Spatial statistics for large data
- Environmental statistics
- Computational statistics
- Weather and climate applications
- Oil and gas emissions and local sensing
- Remote sensing data applications
Daniel McKenzie
- Zeroth-order optimization and applications
- Signal processing, particularly compressed sensing
- Learning-to-optimize for inverse problems
- Nonlinear dimensionality reduction
- First passage percolation
Doug Nychka
- Nonparametric regression
- Spatial statistics
- Computational statistics and big data.
- Novel applications to environmental problems
Samy Wu Fung
- Inverse Problems, Optimization, Deep Learning
- Optimal Control, Mean Field Games