Optimization and Deep Learning
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.
Optimization and Deep Learning Meetings
Optimization and Deep Learning meets on Tuesdays from 11:30 AM to 12:45 PM in CH 156.
Contact Dr. Samy Wu Fung or Dr. Daniel McKenzie if you are interested in attending.
Research Faculty
Daniel McKenzie
email: dmckenzie@mines.edu
website: https://danielmckenzie.github.io/
- Zeroth-order optimization and applications
- Signal processing, particularly compressed sensing
- Learning-to-optimize for inverse problems
- Nonlinear dimensionality reduction
- First passage percolation
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
website: https://inside.mines.edu/~ltenorio/
- Statistical inverse problems
- Applied probability
- Experimental design
