Learning how to learn Markov State Models of conformational dynamics
(in descending order of achievability)
- Understand how Markov State Models are constructed, fit to data, and applied
- Collective coordinate identification
- State space discretization
- Transition operator estimation
- Bayesian sampling of transition matrices
- Potential applications:
- Alpha-synuclein?
- Explore connections between MSM challenges and relavent developments in machine learning
- Esp. interested in Hidden Markov Models, Projected Markov Models, and Reduced-Rank Hidden Markov Models
- Understand and benchmark dimensionality reduction algorithms
- PCA, kPCA
- ICA, tICA, ktICA
- Manifold-learning algorithms
- Diffusion maps?
- Write down design requirements for future learning algorithms
- Understand and benchmark rare-event sampling algorithms
- Transition state theory / transition state sampling
- Transition path theory / transition path sampling
- String method
- Apply geometric Monte Carlo methods to transition-matrix estimation
- Hamiltonian Monte Carlo variants by Mark Girolami and colleagues
- Minor edits, feedback, or contributions to MSMbuilder code + documentation
- Bibliography - papers read / to read
- Notebooks - IPython notebooks containing code + notes on ideas or esp. interesting papers