I do 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
- JAX is cool π