Applied Mathematics and Statistics Research at Mines

Advanced Mathematics and Statistics for the Modern World

The Mines Optimization and Deep Learning (MODL) group conducts research in the intersection of deep learning and optimization. Current areas of interest include inverse problems, learning-to-optimize, applied probability, zeroth order optimization, and implicit deep learning. Our ongoing projects apply these techniques to problems from spatial statistics, optimal power flow, generative models, signal processing, and optimal control. For more information, feel free to contact any of the faculty listed below.

Affiliated Faculty

Ebru Bozdağ

Email: bozdag@mines.edu
Website: https://ebrucsm.wordpress.com/
Research Group: Computational and Global Seismology Group

• Global & Computational Seismology 
• Linearized and non-linear inverse theory 
• 3D (numerical) wave propagation 
• Deep Earth and planetary sciences 
• Seismic hazard 

Greg Fasshauer

Email: fasshauer@mines.edu

• Kernel-based Methods
• Applications of Kernel-based Methods
• Numerical Analysis
• Functional Analysis
• Approximation Theory
• Statistics and Stochastics
• Machine Learning
• Computer-Aided Geometric Design
• Multivariate Splines

Daniel McKenzie

Email: dmckenzie@mines.edu
Website: https://danielmckenzie.github.io/

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

Samy Wu Fung

Email: swufung@mines.edu
Website: https://swufung.github.io/

• Inverse Problems, Optimization, Deep Learning
• Optimal Control, Mean Field Games

Luis Tenorio

Email: ltenorio@mines.edu

• Statistical inverse problems
• Applied probability
• Experimental design