I like to think about probabilistic machine learning research, including:
- deep generative models (normalizing flows, diffusion, flow matching)
- statistical/Bayesian inference (variational inference, MCMC, sampling)
- AI4Science (inverse problems)
Currently on my mind:
- Discrete flow matching π
- Running 100s of MCMC chains on GPUs βοΈ
- How to get rid of LaTeX compilation warnings
β οΈ - JAX is cool π