Applied Mathematics and Statistics Research at Mines

Advanced Mathematics and Statistics for the Modern World

Interpreting data in all its many forms

The main goal of Applied Statistics and Data Science is to interpret and gain insights from 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, which is often as important as the prediction itself when making decisions. The AMS department has projects that cover a wide range of different application areas including many branches of engineering, environmental and earth system science, air quality, and advanced manufacturing. These areas involve statistical methodology such as curve and surface fitting, Bayesian statistics, and statistical computing.

Affiliated Faculty

Soutir Bandyopadhyay

Email: sbandyopadhyay@mines.edu

• Spatial and Environmental Statistics
• Time Series and econometrics
• Bioinformatics
• Bootstrap/Resampling methods
• Large Sample Theory

Dorit Hammerling

Email: hammerling@mines.edu
Website: https://ams.mines.edu/hammerling-research-group/

• Spatial statistics for large data
• Environmental statistics
• Computational statistics
• Weather and climate applications
• Oil and gas emissions and local sensing
• Remote sensing data applications

Doug Nychka

Email: nychka@mines.edu

• Nonparametric regression
• Spatial statistics
• Computational statistics and big data.
• Novel applications to environmental problems

Daniel McKenzie

Email: dmckenzie@mines.edu

• Derivative-free optimization and applications
• Implicit neural networks
• Geometric methods in data science

Nathan Lenssen

Email: lenssen@mines.edu

• Statistical prediction of dynamical systems
• Spatial statistics for geoscience applications
• Large-scale atmospheric and oceanic dynamics
• Impacts of climate change on natural and human systems

Daisy Philtron

Email: dphiltron@mines.edu

• Bayesian modeling of genetic problems in multiple comparisons
• Genetic modifiers of Parkinson’s disease