I like:
- probabilistic machine learning + generative models (diffusion, normalizing flows)
- computational statistics (variational inference, Markov chain Monte Carlo)
- principled understanding of deep learning (inductive biases, scaling laws, generalization)
- AI4Science
Currently on my mind:
- Discrete flow matching π
- Running 100s of MCMC chains on GPUs βοΈ
- How to get rid of LaTeX compilation warnings