Here are some ideas from the wishlist:

• Scholar
  • You can implement some complex algorithms from scikit-learn using Nx. This is novel because not all algorithms have be written in a format that benefits of a tensor compiler and, if you succeed, you should see value perf benefits (plus GPU compilation). I know a student who worked Affinity Propagation to tensors for example as part of their master thesis (in Europe)
• Nx
  • TVM compiler backend
  • Google IREE backend
• Something with reinforcement learning? Double cool if you do something with ALE

This list was used to be so big…

If anyone is tracking this, I built this Gist with slightly more concrete ideas (which were asked by other students): ideas-2023.md · GitHub

Distributed learning may refer to both distributed across multiple GPUs and distributed across machines. In either case, I don’t think there is a lot of work ahead of us or - if I had to guess - not enough for a thesis.

Another area of exploration is additional compilers for Nx. We can target TVM, Google IREE’s and others. There are other ideas, but my wishlist is at home.